Hybrid microgrid system for wind power electrical generation

Abstract

A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ζi, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ζi comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)−ci*(t). The uicnt(t) satisfies

Description

STATEMENT REGARDING PRIOR DISCLOSURE BY THE INVENTORS

Aspects of this technology are described in an article titled “Global sliding-mode control with fractional-order terms for the robust optimal operation of a hybrid renewable microgrid with battery energy storage” in J. Electronics on Dec. 28, 2021, incorporated herein by reference in its entirety.

STATEMENT OF ACKNOWLEDGEMENT

The inventors acknowledge the support provided by the Center of Renewable Energy and Power Systems at King Fahd University of Petroleum & Minerals under Project No. INRE2106 and King Abdullah City for Atomic and Renewable Energy (K.A.CARE).

BACKGROUND

Technical Field

The present disclosure is directed to a non-linear control of a hybrid microgrid system, and more particularly relates to a system and method for controlling a hybrid microgrid system in accordance with global sliding mode control with fractional order terms.

Description of Related Art

The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.

Over past few decades consumption of energy in urban as well as in rural areas has increased due to industrialization, rapid economic development, and social progress. The energy has been generated mostly from fossil fuels. Generating the energy from fossil fuels has certain harmful effects, such as climate change, green house effect, and environmental pollution. On the other hand, over consumption of the fossil fuels depletes natural resources at a fast rate.

Reflecting such circumstances, need for introducing and expanding use of renewable energy sources such as wind, solar, hydro, and geothermal as alternative energy sources has been well recognized, since they are freely and abundantly available, efficient, reliable, and have no adverse environmental effects.

Advances in power electronics technology has led to increase in energy conversion efficiencies and continuing fall in costs of photovoltaic (PV) systems and wind turbines (WT). Global capacities of installed solar and wind power were 586 GW and 623 GW, respectively at the beginning of 2020. See for example, Jamal, S. et al., A review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications, Sustainability 2021, 13, 10331.

The PV systems and WT systems have complementary properties. During the day output from the PV system is high, whereas power generation by WT is low. On the other hand, during the night, solar irradiance is unavailable; the PV system does not generate power, while output power of the WT has tendency to be high. Thus, the PV system and the wind turbines are often tied to a common DC bus or to a common AC bus via boost converters to form a hybrid micro grid system (HMS). See for examples, Alayi, R et al., Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV. Sustainability 2021, 13, 10728, and Al-Quraan, A. et al., Modeling, Design and Control of a Standalone Hybrid PV-Wind Micro-Grid System, Energies 2021, 14, 4849.

The HMS is generally a collection of interlinked renewable and conventional energy resources connected to users and controlled by systems to ensure efficient energy usage and storage. The HMS is also capable to operate in both a grid connected mode and an islanded mode. See for example, Akram, U. et al., An Innovative Hybrid Wind-Solar and Battery-Supercapacitor Microgrid System-Development and Optimization. IEEE Access 2017, 5, 25897-25912, and also https://en.wikipedia.org/wiki/Microgrid.

The HMS is configured to permit insufficiency and uncertainty of the renewale energy sources. However, sometimes the renewable energy produced by the HMS is larger than an energy demand by the users. In such situations, a battery energy storage system (BESS) is utilized to store an excess power. When the energy demand is large and grid connected systems are unable to fulfill the energy demands, the BESS fulfills the energy demand by discharging its stored energy.

A sliding mode control is known as a robust control method applicable to a system with dynamic behaviour. See for examples, V. Utkin, Sliding Mode Control Design Principles and Applications to Electric Drives, IEEE Trans. Industrial Electronics, Vol. 40, No. 1, February 1993, and https://en.wikipedia.org/wiki/Sliding_mode_control, entire contents of both is incorporated by reference herein.

Recently, a global sliding mode control with fractional order operators have been reported in applications to robot control and vehicle control. See, Wang, J. et al., Fractional order sliding mode control via disturbance observer for a class of fractional order systems with mismatched disturbance, Mechatronics 2018, 53, 8-19, and Xie, Y. et al., Coupled fractional-order sliding mode control and obstacle avoidance of a four-wheeled steerable mobile robot, ISA Trans. 2021, 108, 282-294, entire content of both is incorporated by reference herein.

SUMMARY

In an exemplary embodiment, a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a renewable energy source, a grid side converter (GSC) configured to output a power to a point of common coupling (PCC), a DC-link configured to receive a power from the renewable energy source and to supply a power to the GSC, a rechargeable battery configured to exchange a power via the DC-link, a load configured to receive a power via the PCC, a utility grid configured to exchange power via the PCC; and a controller configured to control the HMS by executing a program and in accordance with the method defined as a global sliding mode control with fractional order terms (GSMCFO) method. The program comprises a definition set customized for the HMS and to be referred in applying the GSMCFO method to the HMS.

The method for controlling the HMS includes preparing the definition set including a characteristic element c_i to be measured, and equations defining a desired value c_i* of the characteristic element c_i, a fractional order sliding mode (FOSM) surface (t for the characteristic element c_i, and a control law element u_i^cnt of the characteristic element c_i. The method further includes monitoring the characteristic element c_i(t) and a related status of the HMS. The method further includes calculating at least one of the equations based on the characteristic element c_i(t) and the related status of the HMS monitored. The method further includes controlling the HMS based on the control law element u_i^cnt(t) calculated and in accordance with the GSMCFO method.

The equation defining the FOSM surface ζ_i(t) of the characteristic element c_i(t) comprises a fractional time integral of a tracking error e_i(t) and a fractional time derivative of the tracking error e_i(t). The tracking error e_i(t) for the characteristic element c_i(t) is defined as,e_i(t)=c_i(t)−c_i*(t)Also, the equation defining the control law element u_i^cnt(t) is configured to satisfy a condition,

${\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0,$ so far as ζ_i(t) is not zero.

In another exemplary embodiment, a hybrid microgrid system (HMS) is disclosed. The HMS includes a renewable energy source, a grid side converter (GSC) configured to output a power to a point of common coupling (PCC), a DC-link configured to receive a power from the renewable energy source and to supply a power to the GSC, a rechargeable battery configured to exchange a power via the DC-link; a load configured to receive a power via the PCC; a utility grid configured to exchange a power via the PCC, and a controller including a processor, a memory, a bus-line, and I/O port.

The controller is configured to control the HMS by executing a program installed in the memory and in accordance with a global sliding mode control with fractional order terms (GSMCFO) method. The program includes a definition set customized for the HMS system and to be referred in applying the GSMCFO method to the HMS. The definition set includes a characteristic element c_i to be measured, and the equations defining a desired value c_i* of the characteristic element c_i, a fractional order sliding mode (FOSM) surface (t of the characteristic element c_i, and a control law element u_i^cnt of the characteristic element c_i.

The controller is further configured to monitor the characteristic element c_i(t) and a related status of the HMS, calculate at least one of the equations based on the characteristic element c_i(t) and the related status of the HMS monitored, and control the HMS based on the control law element u_i^cnt(t) calculated and in accordance with the GSMCFO method.

The equation defining the FOSM surface ζ_i(t) for the characteristic element c_i(t) comprises a fractional time integral of a tracking error e_i(t) and a fractional time derivative of the tracking error e_i(t), with e_i(t)=c_i(t)−c_i*(t). Also, the equation defining the control law element u_i^cnt(t) is configured to satisfy a condition:

${\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0,$ so far as ζ_i(t) is not zero.

In another exemplary embodiment, a controller of a hybrid microgrid system (HMS) is disclosed. The HMS includes a renewable energy source, a grid side converter (GSC) configured to output a power to a point of common coupling (PCC), a DC-link configured to receive a power from the renewable energy source and to supply a power to the GSC, a rechargeable battery configured to exchange a power via the DC-link, a load configured to receive a power via the PCC, a utility grid configured to exchange a power via the PCC, and the controller including a processor, a memory, a bus-line, and I/O port.

The controller is configured to control the HMS by executing a program and in accordance with a global sliding mode control with fractional order terms (GSMCFO) method. The program includes a definition set customized for the HMS and to be referred in applying the GSMCFO method to the HMS. The definition set includes a characteristic element c_i to be measured, and equations defining a desired value c_i* of the characteristic element c_i, a fractional order sliding mode (FOSM) surface ζ_i of the characteristic element c_i and a control law element u_i^cnt the characteristic element c_i. In one aspect, the controller is configured to monitor the characteristic element c_i(t) and a related status of the HMS, calculate at least one of the equations defined in the definition set with the program and based on the characteristic element c_i(t₁) and the related status monitored, and control the HMS based on the control law element u_i^cnt(t1) calculated, and in accordance with the GSMCFO.

The equation defining the FOSM surface ζ_i(t) of the characteristic element c_i(t) includes a fractional time integral of a tracking error e_i(t) given by e_i(t)=c_i(t)−c (t) and a fractional time derivative of the tracking error e_i(t). Also, the equation defining the control law element u_i^cnt(t) is configured to satisfy a condition:

${\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0,$ so far as ζ_i(t) is not zero.

The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure, and are not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of this disclosure and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of a hybrid microgrid system, according to certain embodiments;

FIG. 2A is an exemplary graph illustrating a variation pattern for a level of ambient irradiance to a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2B is an exemplary graph illustrating DC-link voltages of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2C is an exemplary graph illustrating an output power from a PV system of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2D is an exemplary graph illustrating an output power from a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2E is an exemplary graph illustrating a grid power supplied from a utility grid and a power demand at a load of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2F is an exemplary graph illustrating a power deviation 4P of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2G is an exemplary graph illustrating battery currents provided from a battery energy storage system to DC-link of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 2H is an exemplary graph illustrating a state of charge of a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 1;

FIG. 3A is an exemplary graph illustrating a variation pattern of wind speed at a hybrid microgrid system according to certain embodiments in Scenario 2.

FIG. 3B is an exemplary graph illustrating rotor speeds of an electric generator of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3C is an exemplary graph illustrating DC-link voltages V_dc of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3D is an exemplary graph illustrating a power generated from a wind turbine generator of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3E is an exemplary graph illustrating a battery power generated from or provided to a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3F is an exemplary graph illustrating a load demand and a grid power of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3G is an exemplary graph illustrating a power deviation of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3H is an exemplary graph illustrating a battery current of a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 3I is an exemplary graph illustrating a state-of-charge (SOC) of a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 2;

FIG. 4A is an exemplary graph illustrating solar irradiance level and wind speed at a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4B is an exemplary graph illustrating DC-link voltages of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4C is an exemplary graph illustrating rotor speeds of a wind turbine generator of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4D is an exemplary graph illustrating powers generated by a wind turbine generator and by a solar photovoltaic system of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4E is an exemplary graph illustrating a load demand and a grid power of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4F is an exemplary graph illustrating a battery power generated from or provided to a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4G is an exemplary graph illustrating a power deviation of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4H is an exemplary graph illustrating a battery current of a battery energy storage system of a hybrid microgrid system according to certain embodiments in Scenario 3;

FIG. 4I is an exemplary graph illustrating a state of charge (SOC) of the BESS, according to certain embodiments in the Scenario 3;

FIG. 5A is an exemplary graph illustrating rotor speeds observed in a hybrid microgrid system with a global sliding mode control with fractional order terms controller according to certain embodiments and observed with a proportional-integral controller in comparison in Scenario 4;

FIG. 5B is an exemplary graph illustrating DC-link voltages observed in a hybrid microgrid system with a global sliding mode control with fractional order terms controller according to certain embodiments and observed with a proportional-integral controller in comparison in Scenario 4;

FIG. 5C is an exemplary graph illustrating battery currents observed in a hybrid microgrid system with a global sliding mode control with fractional order terms controller according to certain embodiments and observed with a proportional-integral controller in comparison in Scenario 4;

FIG. 5D is an exemplary graph illustrating power deviations observed in a hybrid microgrid system with a global sliding mode control with fractional order terms controller according to certain embodiments and observed with a proportional-integral controller in comparison in Scenario 4; and

FIG. 6 is an exemplary illustration of a flowchart for a method of controlling a hybrid microgrid system, according to certain embodiments.

DETAILED DESCRIPTION

In the drawings, like reference numerals designate identical or corresponding parts throughout the several views. Further, as used herein, the words “a,” “an” and the like generally carry a meaning of “one or more,” unless stated otherwise.

Furthermore, the terms “approximately,” “approximate,” “about,” and similar terms generally refer to ranges that include the identified value within a margin of 20%, 10%, or preferably 5%, and any values therebetween.

As described earlier, the HMS is configured to tackles the insufficiency and uncertainty of the renewable energy sources. In order to realize an efficient and cost-effective configuration of the HMS, numbers of power electronics components and power conversion stages have to be minimized. Further, it is also important to harness the maximum power from the PV system and the wind turbine even under astable operating conditions.

Some of the researches have proposed connecting the PV array directly to the common DC bus without using a boost converter in between, so as to minimize the numbers of the power conversion stages and the power electronics components. In such approaches where the boost converter was removed, usually a linear controller, utilizing a linear control mechanism such as proportional integral (PI) control was used for controlling switching operation of other converters, and for maintaining the power balance in the HMS. However, the linear controllers so far reported suffer from one or more drawbacks hindering their adoption.

For example, the linear control mechanism in such cases provides unsatisfactory transients and steady-state performances under circumstances of parametric uncertainties or sudden change in operating point due to fluctuating nature of renewable energy sources and load variations on consumption side. Also, those HMS take a long time to settle in presence of disturbances when the controller follows a linear control mechanism.

Accordingly, it is highly desired to realize an improved control method for the HMS with both a robustness and a short transient time under the circumstances of sudden changes in operating conditions.

A method of controlling the HMS and the HMS with improved efficiency and robustness against varying parameters are disclosed as detailed below. The proposed HMS is constructed with a cost competitive configuration. A controller of the HMS is configured to control the HMS with a global sliding-mode control method with fractional-order terms (GSMCFO) that improves both the transient time and robustness of the HMS, due to greater flexibility of fractional calculus (hereinafter interchangeably referred to as GSMCFO controller).

The GSMCFO controller provides a maximum power point tracking (MPPT) of the PV systems and wind power (WT) generators. Also, the GSMCFO controller provides a) excellent transient and robust steady-state performances of the HMS under parametric uncertainties, b) regulation of DC-link voltage irrespective of the operating conditions, c) extraction of maximum power from both the PV systems and the WT systems with a rotor side converter (RSC), d) active power transfer to the grid via a grid side converter (GSC), and e) maintaining the power balance of the HMS by effective charging/discharging of battery energy storage system (BESS).

FIG. 1 is a schematic diagram of a HMS 100, according to certain embodiments. The HMS 100 includes: a wind turbine (WT) generator including a WT 102 and an electric generator 104, a permanent magnet synchronous generator (PMSG); a rotor side converter (RSC) 106; a DC-link 132; a solar photovoltaic (PV) system 110; a battery energy storage system (BESS) 114; a bidirectional buck-boost converter (BBBC) 112; a grid side converter (GSC) 108; a GSMCFO controller 120; a grid side filter 122 and 124; a point of common coupling 128; a load 126 and a grid 130.

The PMSG 104 includes a stator and a rotor, the latter is mechanically linked with the WT and is configured to be driven by the WT 102 and to convert a mechanical power generated by the WT 102 into an equivalent electrical energy. The RSC 106 is electrically connected with the PMSG 104 and configured to convert an AC current from the output of the PMSG 104 into an equivalent DC current.

The DC-link 132 is electrically connected with an output line of the RSC 106 at one end of the DC-link 132, and with an input line of the GSC 108 at the other end of the DC-link 132. The DC-link 132 is configured to provide a common wired connection, a DC-bus, from the RSC 106 to the GSC for a plurality of other resources. The DC-link 132 includes a capacitor Cdc 133 connected in between the positive terminal and the negative terminal of the DC-link 132.

The PV system 110 is connected to the DC-link 132 directly, that is, without any converters. The PV system 110 is configured to output a DC current that directly adds up to the input current of the DC-link 132. A diode 118 is connected in series with the PV system 110. A positive terminal (P side) of the diode 118 is connected to a positive output terminal of the PV arrays, whereas a negative terminal (N side) of the diode 118 is connected to a positive terminal of the DC-link 132. The diode is configured to prevent a reverse current from the DC-link 132 back to the PV system 110, thereby avoiding reverse-flow damages to the PV system 110. The diode 118 is selected from a group containing, but not limited to a PIN diode, an avalanche diode, a P-N junction diode, a Zener diode, etc. In other implementations, a switch is inserted instead of the diode, where the switch is configured to turn on or off by detecting a voltage difference between the positive output terminal of the PV system and the DC-link.

The BESS 114 is constituted by a rechargeable battery selected from a group containing, but not limited to Lithium-Ion (Li—ON), Nickel-Cadmium (NiCd), Nickel-Metal Hydride (NiMH), Lead-Acid batteries and such batteries. The BESS 114 is configured to supply an energy to the HMS 100 when a load demand, that is, “a power demanded at the load 126 at the PCC” is more than an energy required to be produced by the renewable energy sources of the HMS 100 due to intermittent conditions of the environment.

The BBBC 112 is electrically connected between the BESS 114 and the DC-link 132. In some implementations, for example, the BBBC 112 is a DC-to-DC converter. The BESS 114 is electrically connected to the DC-link 132 through the BBBC 112. The BBBC 112 is configured to operate either in a buck mode (step down) or a boost mode (Step-up). During charging of the BESS 114, the BBBC 112 operates in a buck mode whereas during discharging, the BBBC 112 operates in a boost mode. The buck mode is operated when the renewable energy sources of the HMS 100 generate more power than the load demand. When the HMS 100 generates more power than the load demand, an excess power is stored in the BESS 114 that is used as a reserved power. When the load fluctuates or some of the renewable resources are momentarily unable to supply power due to intermittent environmental condition, the BBBC 112 operates in boost mode to balance the load demand. In boost mode, stored power in the BESS 144 discharges, which is provided to the load in order to balance the load demand and avoid a load shedding.

The GSC 108 is configured to convert a DC voltage over the DC-link 132 to an AC output voltage/current. The output line of the GSC 108 is configured to supply the AC output current to a point of a common coupling (PCC) 128 via a filter including an inductor Lf 122 and a resistor R_f 124 connected in series.

A load 126 connected to the PCC is configured to receive power from the PCC. The PCC is also connected with a grid 130 and is configured to receive or transfer a power from/to the grid 130. A “power grid” generally means an interconnected local or wide area public network for electricity delivery. The grid 130 here represents an access point to the power grid. Optionally, the HMS is configured isolated from the grid to form an islanded HMS. The load 126 refers to a common load due to residential consumption, industry consumption and the like. The load in residential consumption includes lighting instruments, air conditioners, etc. The load in industry consumption includes machineries etc.

The GSMCFO controller 120 or simply the controller 120 is configured to control the HMS 100 in accordance with a global sliding-mode control utilizing a fractional-order terms (GSMCFO). The controller 120 is configured to achieve the MPPT of both the PV system 110 and the WT generator system 102 and 104, regulation of the voltage of the DC-link 132, and power balance of the HMS by controlling the battery current I_b with excellent transient and steady-state performances even under disturbances and uncertainties in the ambient environment.

The controller 120 includes a processor 120-1, a memory 120-2 with a preinstalled software, input ports 120-3 and output ports 120-5, and a bus line 120-4. The preinstalled software includes an instruction set and a definition set. The controller is configured to fetch the instruction set to the processor 120-1, execute them referring the definition set and based on a monitored status of the HMS.

The instruction set includes instructions for steps to be executed in controlling the HMO in accordance with the GSMCFO. The steps to be executed include monitoring a current status of the HMS, calculating equations defined in the definition set based on the current status monitored, and controlling the HMS based on calculation results.

The definition set includes a characteristic element c_i to be monitored, a desired value c_i* of the characteristic element c_i, and equations defining a fractional order sliding mode (FOSM) surface (t of the characteristic element c_i, an equivalent control law element u_i^eqv of the characteristic element c_i, and a control law element u_i^cnt of the characteristic element c_i.

The instruction set also includes an instruction for performing iteration of those steps or part of those steps until the FOSM surface ζ_i or the tracking error e_i(t) reduces to values equal to or smaller than each criterion predetermined.

The characteristic elements are selected based on modeling of each of constituents of the HMS. The characteristic elements selected for the WT generator system 102 and 104 are an angular frequency ω_r (=c₁) of the WT and a d axis stator current I_ds(=c₂) of the PMSG 104. The characteristics element selected for the DC-link 132 is a DC-link voltage V_dc(=c₃). The characteristics element selected for the GSC output are a d-axis AC current I_d(=c₄) and a q-axis AC current I_q(=c₅). The characteristic element selected for the BBBC 112 is a battery current I_b (=c₆).

Preliminaries for GSMCFO Method

Definitions and properties of the fractional-order calculus are briefly reviewed here. The GSMCFO fully utilizes the fractional-order calculus.

A Riemann-Liouville fractional derivation of order μ(0<μ<1) of a continuous function ϕ(t) is defined by:

$\begin{array}{cc}{ }_{t_0}^R{t}_{\mu }^{}\varphi \left(t\right)=\frac{1}{\Gamma \left(m-\mu \right)}\frac{d^m}{{\mathrm{dt}}^m}{\int }_{t_0}^t\frac{\varphi \left(\tau \right)}{{\left(t-\tau \right)}^{\mu -m+1}}d\tau ,& \left(1\right)\end{array}$ where, m is a positive integer number, Γ is a Gamma function defined as,Γ(q)=∫₀^∞x^q-1e^−xdx  . (2)

On the other hand, a Riemann-Liouville fractional integration of order μ(0<μ<1) of a continuous function ϕ(t) is described by:

$\begin{array}{cc}{ }_{t_0}^R{t}_{\mu }^{}\varphi \left(t\right)=\frac{1}{\Gamma \left(\mu \right)}{\int }_{t_0}^t\frac{\varphi \left(\tau \right)}{{\left(t-\tau \right)}^{1-\mu }}d\tau .& \left(3\right)\end{array}$ Below three properties of the fractional-order calculus are noticeable.

• • Property 1: If 0<μ<1, then:^c_tot^μ(^c_tot^μϕ(t))=ϕ(t).  (4)
  • Property 2: If 0<μ<1, then:^c_toD_t^μ-1(^c_tot^μϕ(t))={dot over (ϕ)}(t).  (5)
  • Property 3: The fractional derivation or integration is a linear operation:D_t^μ(ϕ₁(t)+ϕ₂(t))=D_t^μϕ₁(t)+D_t^μϕ₂(t).  (6)Modeling of the WT Generator and Application of the GSMCFO Method

The mechanical power captured by the wind turbine (WT) 102 from the wind is given by:

$\begin{array}{cc}{P}_w=\frac{1}{2}\rho \pi R^2{C}_p\left(\lambda ,\beta \right)V_w^3;& \left(7\right)\end{array}$ $\begin{array}{cc}{C}_p\left(\lambda ,\beta \right)=0.5176\left(\frac{116}{{\lambda }_i}-0.4\beta -5\right)e^{\frac{-21}{{\lambda }_i}}+0.0068\lambda ;& \left(8\right)\end{array}$ $\begin{array}{cc}\frac{1}{{\lambda }_i}=\frac{1}{\lambda +0.08\beta }-\frac{0.035}{{\beta }^3+1};& \left(9\right)\end{array}$ $\begin{array}{cc}\lambda =\frac{R{\omega }_r}{V_w};& \left(10\right)\end{array}$ $\begin{array}{cc}\frac{d{\omega }_r}{\mathrm{dt}}=J^{-1}P\left[T_m-T_e\right],& \left(11\right)\end{array}$ where P_W is a mechanical power captured by the WT 102, V_w is a wind speed, C_p is a power coefficient, ρ is an air density, R is a radius of the WT 102, ω_r is an angular speed of the WT 102, a tip-speed ratio is represented by λ, β is a pitch angle, J is an inertia of a mechanical shaft, P is a number of pole pairs, and T_e and T_m are an electrical and a mechanical torque, respectively. See for example, Housseini, B. et al., Robust Nonlinear Controller Design for On-Grid/Off-Grid Wind Energy Battery-Storage System. IEEE Trans. Smart Grid 2018, 9, 5588-5598, entire contents of which is incorporated herein by reference.

Now, assuming a desired tip speed ratio λ* gives a maximum value of the power coefficient C_p(λ, β) of Eq. (8) for any given wind speed V_W, a desired angular speed ω_r*of the WT, namely the desired value of the characteristic element c_i* is given from Eq. (4) as:

$\begin{array}{cc}{c}_1^{*}={\omega }_r^{*}=\frac{{\lambda }^{*}{V}_w}{R}.& \left(12\right)\end{array}$

The voltage and the torque dynamics of the PMSG 104 in the d-q reference frames are given as:

$\begin{array}{cc}{V}_{ds}=L_d\frac{{\mathrm{dI}}_{ds}}{\mathrm{dt}}-{\omega }_r{L}_q{I}_{qs}+R_s{I}_{ds};& \left(13\right)\end{array}$ $\begin{array}{cc}{V}_{qs}=L_q\frac{{\mathrm{dI}}_{qs}}{\mathrm{dt}}+{\omega }_r{L}_d{I}_{ds}+{\omega }_r{\Lambda }_r+R_s{I}_{qs};& \left(14\right)\end{array}$ $\begin{array}{cc}{T}_e=\frac{3P}{2}\left[\left(L_d-L_q\right)I_{ds}{I}_{qs}+{\Lambda }_r{I}_{qs}\right],& \left(15\right)\end{array}$ where V_ds and V_qs are the d-axis and q-axis stator voltages of the PMSG 104, respectively, L_d and L_q are the d-axis and q-axis self-inductances, respectively, I_ds and I_qs are the d-axis and q-axis stator currents, respectively, L_q is the q-axis self-inductance, Λ_r is the rotor flux, and R_s is the stator resistance. As the PMSG 104 is a non-salient PMSG 104, L_d and L_q are equal, the electrical torque T_e is given as:

$\begin{array}{cc}{T}_e=\frac{3P}{2}{\Lambda }_r{I}_{qs}.& \left(16\right)\end{array}$

To minimize the stator iron loss and maximize the efficiency of the PMSG 104, the d-axis stator current is preferably be zero. Thus, the desired value for the characteristic element c₂* given by the desired d-axis stator current is set to zero:C₂*=I_ds*=0.  (17)

A tracking error e₁(t) for the characteristic element c_i, that is, the angular frequency ω_r of the WT and a tracking error e₂(t) for the characteristic element c₂, that is the d-axis stator current I_ds at a time t are defined as,e₁(t)=ω_r(t)−ω_r*(t),  (18)e₂(t)=I_ds(t)−I_ds*(t)=I_dS(t).  (19)Further taking time derivations, following relations are obtained:

$\begin{array}{cc}{\stackrel{.}{e}}_1\left(t\right)=\frac{P}{J}\left[T_m-T_e\right]-{\dot{\omega }}_r^{*}\left(t\right);& \left(20\right)\end{array}$ $\begin{array}{cc}{\ddot{e}}_1\left(t\right)=a\left[L_q{\omega }_r{I}_{ds}+R_s{I}_{qs}+{\lambda }_r{\omega }_r\right]+a{V}_{qs}+H_1-{\ddot{\omega }}_r^{*}\left(t\right);& \left(21\right)\end{array}$ $\begin{array}{cc}{\stackrel{.}{e}}_2=\frac{1}{L_d}\left[L_q{\omega }_r{I}_{qs}-R_s{I}_{ds}\right]+\frac{1}{L_d}{V}_{ds}+H_2-I_{ds}^{*}\left(t\right),& \left(22\right)\end{array}$ where,

$a=\frac{3P^2{\Lambda }_r}{2{\mathrm{JL}}_q},H_1$ and H₂ represent lumped disturbances in the system.

Now, fractional-order sliding-mode (FOSM) surfaces ζ₁(t) and ζ₂(t) are defined based on the tracking errors e₁(t) and e₂(t), for the angular speed ω_r and for the d-axis stator current I_ds, respectively, as below.ζ₁(t)=k₁^R_t^μe₁(t)+σ₁^R_t^1-μe₁(t)+^RD_t^2-μe₁(t),  (23)ζ₂(t)=k₂^R_t^μe₂(t)+^RD_t^1-μe₂(t),  (24)where μ ∈(0, 1), and k₁, k₂ and σ₁ are positive constants, ^RJ_t^μ denotes a Riemann-Liouville fractional integration, ^RD_t^1-μ and ^RD_t^2-μ denote Riemann-Liouville fractional derivations.

Taking the Riemann-Liouville fractional derivations of ζ₁(t) and ζ₂(t),^RD_t^μζ₁(t)=k₁e₁(t)+σ₁ė₁(t)+ë₁(t),  (24)^RD_t^μζ₂(t)=k₂e₂(t)+ė₂(t).  (25)

Replacing ë₁(t) and ė₂(t) in the above two equations by inserting (21) and (22), the equations (24) and (25) can be rewritten as,^Rt₁^μ(t)=k₁e₁(t)+ϕ₁ė₁(t)+H₁+α[L_qω_rI_ds+R_sI_qs+λ_rω_r]+αV_qs−ω_r*,  (26)

$\begin{array}{cc}{ }^R{t}_{\mu }^{}{\zeta }_2\left(t\right)=k_2{e}_2\left(t\right)+\frac{1}{L_d}\left[L_q{\omega }_r{I}_{qs}-R_s{I}_{ds}\right]+\frac{V_{ds}}{L_d}+H_2-{\stackrel{.}{I}}_{ds}^{*}.& \left(27\right)\end{array}$

Here, the equivalent control law elements u₁^eqv(t) and u₂^eqv(t) of the characteristic elements c₁ (=ω_r) and c₂ (=I_ds) are derived from conditions giving ^R_t^μζ_i(t)=0, (I=1, 2) in Eqs. (26) and (27), and chosen as q-axis stator voltage V_qs and d-axis stator voltage V_ds, respectively, referring the two equations. The equations for u₁^eqv(t) and u₂^eqv(t) are obtained by further replacing H_i(i=1, 2) with ^R_t^1-μδ_i(i=1, 2), respectively, as follows:

$\begin{array}{cc}{u}_1^{eqv}\left(t\right)=V_{qs}^{eqv}\left(t\right)=-\frac{1}{a}\left(\begin{array}{c}{k}_1{e}_1\left(t\right)+{\sigma }_1{\dot{e}}_1\left(t\right)+{ }_{}^R{t}_{1-\mu }^{}{\delta }_1-{\ddot{\omega }}_r^{*}+\\ a\left[L_q{\omega }_r{I}_{ds}+R_s{I}_{qs}+{\lambda }_r{\omega }_r\right]\end{array}\right),& \left(28\right)\end{array}$ $\begin{array}{cc}{u}_2^{eqv}\left(t\right)=V_{ds}^{eqv}\left(t\right)=-L_d\left(\begin{array}{c}{k}_2{e}_2\left(t\right)+\frac{\left[L_q{\omega }_r{I}_{qs}-R_s{I}_{ds}\right]}{L_d}+\\ { }_{}^R{t}_{1-\mu }^{}{\delta }_2-{\stackrel{.}{I}}_{ds}^{*}\end{array}\right).& \left(29\right)\end{array}$ Here the lumped external disturbances and parametric perturbations H_i in the HMS, are assumed to satisfy below conditions:∥H_i∥≤^R_t^1-μδ_i, and ∥^R_t^1-μH_i∥≤δ_i,  (30)where δ_i is a positive function.

Now, the control law elements u₁^cnt(t) and u₂^cnt(t) of the characteristic elements c₁ (ω_r) and c₂ (=I_ds) are defined as below based on the equivalent control law elements Eqs. (28) and (29):

$\begin{array}{cc}{u}_1^{\mathrm{cnt}}\left(t\right)=V_{qs}^{\mathrm{cnt}}\left(t\right)=V_{qs}^{eqv}\left(t\right)-\frac{1}{a}\left[{\varrho }_I{ }_{}^R{t}_{1-\mu }^{}\mathrm{SG}\left({\zeta }_1\left(t\right)\right){❘{\zeta }_1\left(t\right)❘}^{\alpha }+{\gamma }_1{ }_{}^R{t}_{1-\mu }^{}{\zeta }_1\left(t\right)\right];& \left(31\right)\end{array}$ $u_2^{\mathrm{cnt}}\left(t\right)=V_{ds}^{\mathrm{cnt}}\left(t\right)$ $\begin{array}{cc}=V_{ds}^{eqv}\left(t\right)-L_d\left[{\varrho }_2{ }_{}^R{t}_{1-\mu }^{}SG\left({\zeta }_2\left(t\right)\right){❘{\zeta }_2\left(t\right)❘}^{\alpha }+{\gamma }_2{ }_{}^R{t}_{1-\mu }^{}{\zeta }_2\left(t\right)\right],& \left(32\right)\end{array}$ where, α∈(0, 1), _i and γ_i(I=1, 2) are positive constants, and SG (ζ_i(t)) (i=1, 2) represent signum function sgn(ζ_i(t)) and its smooth approximations, for example,

$\tan h\left(\frac{{\zeta }_i\left(t\right)}{\vartheta }\right)$ with ϑ>0.

It is noted that the FOSM surfaces ζ_i(t), (i=1, 2) and time derivatives of the FOSM surfaces

$\frac{d}{\mathrm{dt}}{\zeta }_i\left(t\right),\left(i=1,2\right)$ satisfy below relation Eq. (33) when the control law element u_i^cnt(t), (i=1, 2) are applied in controlling the WT and the PMSG.

$\begin{array}{cc}\begin{array}{c}{\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}={\zeta }_i\left(t\right){ }_{}^R{t}_{1-\mu }^{} {(}_{}^R{t}_{\mu }^{}{\zeta }_i\left(t\right))\\ ={\zeta }_i\left(t\right){ }_{}^R{t}_{1-\mu }^{}\left(\begin{array}{c}{H}_i-{ }_{}^R{t}_{1-\mu }^{}{\delta }_i\\ -{\varrho }_i^R{t}_{1-\mu }^{}\mathrm{SG}\left({\zeta }_i\left(t\right)\right)-{\gamma }_i{ }_{}^R{t}_{1-\mu }^{}{\zeta }_i\left(t\right)\end{array}\right)\\ ={\zeta }_i\left(t\right){ }_{}^R{t}_{1-\mu }^{}{H}_i-{\delta }_i)-{\varrho }_i{\zeta }_i\mathrm{sgn}\left({\zeta }_i\left(t\right)\right){❘{\zeta }_i\left(t\right)❘}^{\alpha }-{\gamma }_i{\zeta }_i^2\left(t\right)\\ \le -{\varrho }_i❘\mathrm{sgn}\left({\zeta }_i\left(t\right)\right)❘{❘{\zeta }_i\left(t\right)❘}^{\alpha +1}-{\gamma }_i{❘{\zeta }_i\left(t\right)❘}^2\\ <0.\left(\mathrm{when}{\zeta }_i\left(t\right)\ne 0\right)\end{array}& \left(33\right)\end{array}$

As described above, the GSMCFO method includes calculations of the FOSM surface ζ_i(t) and the control law elements u_i^cnt(t) including a fractional time integral and a fractional time differentiation of the tracking errors e₁ and e₂. The controller is configured to monitor a status of the HMS, including the characteristics elements c₁ (=ω_r) and the rotor current c₂ (=I_ds), and configured to control the RSC 106 by applying the control law elements u_i^cnt(t), (i=1, 2) obtained by fractional calculus, when disturbances are perceived. The control law elements guarantee the characteristics elements ω_r and I_ds to effectively arrive and stay on the sliding mode surfaces ζ₁(t) and ζ₂ (t), in the presence of external disturbances, parametric perturbation, and unknown dynamics.

When the controller 120 applied the control law elements u_i^cnt(t), (i=1, 2), a condition:

${\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0$ is satisfied, as far as ζ_i(t) is nonzero. Whenever the condition was satisfied, it is an indication that the angular frequency ω_r and the d-axis stator rotor current I_ds are approaching the FOSM surface ζ_i(t), and sliding along the FOSM surface toward a global stabilized point satisfying

${\zeta }_i\left(t\right)=\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}=0,$ (i=1, 2).

However, when there is a momentary reduction in the operating frequency ω_r of the rotor or the rotor current I_ds, the error function e(t) is detected as non-zero. This indicates a variation of the operating frequency ω_r of the rotor or the current I_ds through the rotor are different compared to the desired rotational frequency ω_r or the rotor current I_ds. The momentarily reduction may be due to intermittent noise in the atmosphere such as wind speed reduction for a certain duration. At this time, the program in the definition set in the controller 120 executes multiple equations containing the error function, FOSM surface calculation based on the error function as well as control law element based on the equations of the FOSM surface. Based upon the value of the control law element u₁^cnt(t), the controller 120 signals to the RSC 106 to modify the pulse width modulation signals to track the next optimum operating point of the rotor to harness the maximum wind power again. Since the control law element u₁^cnt(t) allows ω_r and I_ds to effectively arrive and stay on the sliding mode surfaces ζ₁(t) and ζ₂(t), the modified pulse width modulation signals are supplied to the RSC 106, such that the RSC 106 may generate an equivalent proportional current or voltage at its output, even in the presence of atmospheric disturbances, parametric perturbation, or any unknown dynamics. For example, if initially the WT 102 was generating 50V DC voltage at the output of the RSC 106 at the specific ω_r and I_ds, the WT 102 is now generating a reduced voltage (for example, 47 V) in presence of atmospheric disturbances. However, since the FOSM surfaces are highly convergent, the system quickly attains the new stable points, that is, 47 V, without any glitch or overshoot at the output of the RSC 106 because of the fractional sliding mode surfaces ζ₁(t) and ζ₂(t).

Modeling of the PV System

The PV system 110 is formed by an arrangement of PV cells (consisting of n- and p-type semiconductor materials) in a framework. The PV cells generate a DC voltage from solar radiation. An equivalent circuit of a PV cell includes a current source, a diode, a series resistor, and a parallel resistor. An output current of the PV system 110 is given by,

$\begin{array}{cc}{I}_{pv}=N_p\left(I_{ph}-I_S\left[\exp \left(\frac{q{V}_{dio}}{N_{s}A{k}_{B}T}\right)-1\right]-\frac{V_{dio}}{R_{sh}}\right),& \left(34\right)\end{array}$ $\begin{array}{cc}{V}_{dio}=\frac{V_{pv}+I_{pv}\frac{N_s}{N_p}{R}_{se}}{N_s},& \left(35\right)\end{array}$ where,where I_ph is a photo-generated current, I_pv is the PV output current, I_s is the reverse saturation current, N_s and N_p are the number of series- and parallel-connected PV cells, q is the electron charge, V_dio is a diode voltage, A represents a p-n junction factor, k_B represents the Boltzmann constant, T is a cell temperature, and R_se and R_sh are equivalent series and shunt resistances, respectively.MPPT Operation of the PV System and Regulating the DC Link Voltage

The controller 120 is configured to perform the maximum power point tracking (MPPT) operation of the PV system 110 at different irradiance levels using an advanced multi-sector perturb and observe approach, and configured to compute corresponding MPPT voltage V_pv^MPPT and current I_pv^MPPT, simultaneously. See, for example, Youssef, A. R. et al., Advanced multi-sector P&O maximum power point tracking technique for wind energy conversion system. Int. J. Electr. Power Energy Syst. 2019, 107, 89-97, entire contents of which is incorporated herein by reference. The MPPT voltage, V_PV^MPPT is set as the reference voltage for the DC-link V_dc*. When the irradiance level is very low (at night), a V_pv^MPPT is also low (below the minimum allowable DC-link voltage), then the controller 120 is configured to set the V_dc* as the nominal DC-link voltage V_dc*. On the other hand, when the irradiance level is too high, a V_pv^MPPT is also high. If the V_pv^MPPT was greater than the allowable maximum DC-link voltage, the controller is configured to set the V_dc* as the nominal DC voltage. The HMS can be configured to control the MPPT operation of the controller via the control of the GSC 108.

Modeling of the DC-Link and Application of GSMCFO Method

The power generated from the renewable energy sources in the HMS 100 is transferred to the GSC through the DC-link 132. A dynamic model of the DC-link voltage V_dc (=c₃) of the DC-link 132 is derived as follows:

$\begin{array}{cc}{C}_{dc}\frac{d{V}_{dc}}{\mathrm{dt}}=I_{sdc}+I_{pv}+\left(1-D\right)I_b-I_{\mathrm{gdc}},& \left(36\right)\end{array}$ where C_dc is the capacitance of the DC-bus, I_sdc is the output DC current from the RSC 106, I_pv is the output current from the PV system, I_b is the battery current from the BESS 114, D is the duty cycle of the BBBC 112 for the BESS 114, and I_gdc is a DC current input to the GSC 108.

Assuming that the RSC 106 and GSC 108 are ideal, the following relations are valid:P_w=I_sdcV_dc,  (37)

$\begin{array}{cc}{I}_{gdc}{V}_{dc}=\frac{3}{2}{V}_d{I}_d,& \left(38\right)\end{array}$ where I_d and V_d are the d-axis AC current and AC voltage of the AC output of the GSC 108, respectively. Using the above equation, the DC voltage dynamic equation can be rewritten as:

$\begin{array}{cc}{C}_{dc}\frac{d{V}_{dc}}{\mathrm{dt}}=\frac{P_w}{V_{dc}}+I_{pv}+\left(1-D\right)I_b-\frac{3}{2}\frac{V_d{I}_d}{V_{dc}}.& \left(39\right)\end{array}$

The characteristics element c₃ is selected as the DC-link voltage V_dc. Thus, the controller 120 is configured to monitor the DC-link voltage V_dc. A tracking errors e₃(t) for the DC-link voltage is given by:e₃(t)=V_dc(t)−V_dc(t),  (40)where, V_dc*(t) represents a desired value for the DC-link voltage V_dc.

The desired value V_dc* defined for the DC-link voltage V_dc is given as,

$\begin{array}{cc}{c}_3^{*}\left(t\right)=V_{dc}^{*}=\{\begin{array}{cc}{V}_{pv}^{\mathrm{MPPT}}& ;\mathrm{when}{V}_{\mathrm{dc}}^{\min }\le V_{pv}^{\mathrm{MPPT}}\le V_{\mathrm{dc}}^{\max },\\ V_{\mathrm{dc}}^{nom}& ;\mathrm{when}{V}_{pv}^{\mathrm{MPPT}}<V_{\mathrm{dc}}^{\min },\mathrm{or}{V}_{pv}^{\mathrm{MPPT}}>V_{\mathrm{dc}}^{\max },\end{array}& \left(15\right)\end{array}$ wherein, V_pv^MPPT represents an output voltage of the PV system under a maximum power point tracking (MPPT) operation, V_dc^min, V_dc^max and V_dc^nom represent a minimum allowable value, a maximum allowable value, and a nominal value of the DC-link voltages, each predetermined respectively.

Thus, the tracking error e₃(t) is defined as a difference between a monitored voltage of the DC-link 132 and a desired voltage of the DC-Link 132.

A time derivative of the tracking error e₃(t) is given using Eq. (39) as below:

$\begin{array}{cc}{\dot{e}}_3\left(t\right)=\frac{P_w}{C_{\mathrm{dc}}{V}_{\mathrm{dc}}}+\frac{I_{pv}}{C_{\mathrm{dc}}}+\frac{\left(1-D\right)}{C_{\mathrm{dc}}}{I}_b-\frac{3}{2}\frac{V_d{I}_d}{C_{\mathrm{dc}}{V}_{\mathrm{dc}}}+H_3-{\dot{V}}_{\mathrm{dc}}^{*}\left(t\right),& \left(41\right)\end{array}$ where H₃ represents the additive lumped disturbance in the HMS 100.

The FOSM surface ζ₃(t) for the DC-link voltage V_dc is defined as:ζ3(t)=k₃R_t^μe₃(t)+^R_t^1-μe₃(t),  (42)where μ(0<μ<1) and k₃ are positive constants, ^R_t^μ denotes a Riemann-Liouville fractional integration, ^R_t^1−μ denotes a Riemann-Liouville fractional derivations.

Taking a Riemann-Liouville fractional derivation of ζ₃(t) gives:_t^μζ₃(t)=k₃e₃(t)+ė₃(t).  (43)Inserting Eq. (41) for ė₃(t) into the above equation gives:

$\begin{array}{cc}{ }_{}^R{t}_{\mu }^{} {\zeta }_3\left(t\right)=k_3{e}_3\left(t\right)+\frac{P_w}{C_{\mathrm{dc}}{V}_{\mathrm{dc}}}+\frac{I_{\mathrm{pv}}}{C_{\mathrm{dc}}}+\frac{\left(1-D\right)}{C_{\mathrm{dc}}}{I}_b-\frac{3}{2}\frac{V_d{I}_d}{C_{\mathrm{dc}}{V}_{\mathrm{dc}}}+H_3-{\dot{V}}_{\mathrm{dc}}^{*}\left(t\right).& \left(44\right)\end{array}$

Here, the equivalent control law elements u₃^eqv(t) of the characteristic elements c₃ (=V_dc) is derived from the conditions giving ^R_t^μζ_i(t)=0, (i=3) in Eq. (44), and is selected as the d-axis AC current I_d of the AC output from the GSC 108, referring the equation. The equations for the equivalent control law elements u₃^eqv(t) is obtained by further replacing the external lumped disturbance H_i(i=3) with ^R_t^1-μδ_i(i=3), as follows:

$\begin{array}{cc}{u}_3^{eqv}\left(t\right)=I_d^{eqv}\left(t\right)=\frac{2}{3}\frac{C_{\mathrm{dc}}{V}_{\mathrm{dc}}}{V_d}\left(\begin{array}{c}{k}_3{e}_3\left(t\right)+\frac{P_w}{C_{\mathrm{dc}}{V}_{\mathrm{dc}}},+\frac{I_{pv}}{C_{\mathrm{dc}}}\\ +\frac{\left(1-D\right)}{C_{\mathrm{dc}}}{I}_b+{ }_{}^R{t}_{1-\mu }^{}{\delta }_3-{\dot{V}}_{\mathrm{dc}}^{*}\left(t\right)\end{array}\right),& \left(45\right)\end{array}$ where, ^R_t^1−μ δ₃ represents external perturbations with a positive function δ₃, and assumed to satisfy the condition given by Eq. (30).

Now, the control law elements u₃^cnt(t) of the characteristic elements c₃ (=I_d) is defined based on the equivalent control law element u₃^eqv(t) in Eq. (45) as:

$\begin{array}{cc}{u}_3^{\mathrm{cnt}}\left(t\right)=I_d^{\mathrm{cnt}}\left(t\right)=I_d^{eqv}\left(t\right)+\frac{2}{3}\frac{C_{dc}{V}_{dc}}{V_d}\left[{\varrho }_3{ }_{}{t}_{1-\mu }^{}SG\left({\zeta }_3\left(t\right)\right){❘{\zeta }_3\left(t\right)❘}^{\alpha }+{\gamma }_3{ }_{}{t}_{1-\mu }^{}{\zeta }_3\left(t\right)\right],& \left(46\right)\end{array}$ where, α ∈ (0, 1), ₃ and γ₃ are positive constants, and SG(ζ_i(t)) (i=3) represents a signum function sgn(ζ_i(t)) and its smooth approximations, for example,

$\tanh \left(\frac{{\zeta }_i\left(t\right)}{\vartheta }\right)$ with ϑ>0.

As described above, the GSMCFO method applied in controlling the DC-link 132 of the HMS 100 includes calculations of the FOSM surface ζ₃(t) and the control law elements u₃^cnt(t) including a fractional time integral and a fractional time derivative of the tracking error e₃. The controller is configured to monitor a status of the HMS, including the characteristics element c₃, that is, the DC-link voltage V_dc, and configured to control the GSC 108 by applying the control law element u₃^cnt(t) (=I_d^cnt(t)) defined for the d-axis AC current I_d of the GSC 108 and obtained by the fractional calculus, when disturbances are perceived. The control law element u₃^cnt(t) (=I_d^cnt(t)) guarantees the characteristic element c₃ (=V_dc) effectively arrive and stay on the FOSM surfaces ζ3(t) in the presence of external disturbances, parametric perturbation, and unknown dynamics.

Applying the GSMCFO method and the control law elements u₃^cnt(t) by the controller 120 realizes a condition:

${\zeta }_3\left(t\right)\frac{d{\zeta }_3\left(t\right)}{\mathrm{dt}}<0,$ as far as ζ₃(t) is nonzero, as discussed earlier. Whenever the condition is satisfied, it is an indication that the characteristics element C₃ (=V_dc) of the GSC 108 is approaching the FOSM surface ζ₃(t), and sliding along the FOSM surface toward a global stabilized point satisfying

${\zeta }_3\left(t\right)=\frac{d{\zeta }_3\left(t\right)}{\mathrm{dt}}=0.$

It is clear from the above equations that the controller 120 includes the FOSM surface having integration and a differentiation terms of the errors e₃. In other words, the equation defining the FOSM surface ζ₃(t) of the characteristic element c₃(t) comprises a fractional time integral of the tracking error e₃(t) given by e₃(t)=c₃(t)−c₃*(t) and a fractional time derivative of the tracking error e₃(t).

The equivalent control law element and the control law element u₁^cnt(t) are the controlling signals to control the voltage of the DC-link 132 when the disturbances are perceived in the DC-link 132 due to any unknown reason. The control law element and the equivalent control law element makes e₃ reach and remain on the surface ζ₃(t)=0 in the presence of external disturbances, parametric perturbation, and unknown dynamics.

Physical Implication

The controller 120 monitors the V_dc through the DC-link 132. The controller 120 also executes the programmatic instructions containing equations of FOSM and the equivalent control law and the control law element whenever, the control law element u_i^cnt(t) satisfies a condition

$\begin{array}{cc}{\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0,& \left(47\right)\end{array}$ so far as ζ_i(t) is nonzero. Whenever the condition satisfies, it is an indication to the controller 120 that the value of V_dc is optimum and no correction in the Pulse width modulation signals to track the optimum operating point of the PV system 110 to harness the maximum solar power is needed. At this moment, the error function e(t) is detected as zero.

However, when there is a momentarily reduction in the V_dc, the error function e₃(t) is detected as non-zero. This indicates a variation of the V_dc compared to the desired voltage V_dc* of the DC-link 132. The momentarily reduction may be due to intermittent noise in the atmosphere such as cloudy environment for a certain duration. At this time, the program in the definition set in the controller 120 executes multiple equations containing the error function, FOSM surface calculation based on the error function as well as control law element based on the equations of the FOSM surface. Based upon the value of the control law element u₃^cnt(t), the controller 120 signals to the GSC 108 to modify the pulse width modulation signals to track the next optimum operating point of the PV system to harness the maximum solar power again. Since the control law element u₃^cnt(t) allows V_dc to effectively arrive and stay on the sliding mode surface ζ₃(t), the modified pulse width modulation signals are supplied to the GSC 108, such that the GSC 108 may generate an equivalent proportional current or voltage at its output, even in the presence of atmospheric disturbances, parametric perturbation, or any unknown dynamics. For example, initially the PV system 110 was generating 45V DC voltage to the DC-link 132, is it now generating a reduced voltage (for example, 39 V) in presence of atmospheric disturbances. However, since the FOSM surfaces are highly convergent, the system quickly attains the new stable points, without any glitch or overshoot at the input side of the GSC 108 because of the fractional sliding mode surface ζ₃(t).

Modeling of the GSC and Application of GSMCFO Method

The GSC 108 of the HMS 100 is configured to (a) regulate the DC-link voltage, (b) achieve the MPPT performance of the solar PV system, (c) ensure power transfer from the DC side to the AC grid side, and (d) to regulate the active and reactive power at the AC grid side. A cascade control structure is adopted in certain implementations.

Dynamic equations of the GSC in the d-q reference frames can be expressed as follows:

$\begin{array}{cc}{V}_d=U_d-L_f{\omega }_g{I}_q+R_f{I}_d+L_f\frac{{\mathrm{dI}}_d}{\mathrm{dt}},& \left(48\right)\end{array}$ $\begin{array}{cc}{V}_q=U_q+L_f{\omega }_g{I}_d+R_f{I}_q+L_f\frac{{\mathrm{dI}}_q}{\mathrm{dt}},& \left(49\right)\end{array}$ where I_d and I_q represent the d-axis and the q-axis AC currents of an AC output of the GSC 108, respectively, and V_d and V_q, the d-axis and the q-axis AC voltages of the output of the GSC 108, respectively, U_d and U_q are the d-axis and q-axis voltages at the PCC 128, L_f and R_f are an inductance 122 and a resistance 124 of the grid side filter, respectively and ω_g is an electrical angular frequency of the output of the GSC 108.

The AC currents of the AC output the GSC are controlled for proper power transfer to the grid side. The active power is controlled by the d-axis AC current. Thus, the desired d-axis current I_d* is given by I_d. The reactive power is desired to be very close to zero by controlling the q-axis current and setting the desired q-axis current as I_q*=0.

Thus, the characteristics elements c_i(t) (i=4, 5) are defined for the GSC are the d-axis AC current I_d (=c₄(t)) of the AC output from the GSC 108 for i=4, and the q-axis AC current I_q (=c₅(t)) of the AC output from the GSC 108 for i=5. The controller 120 is configured to monitor the characteristic elements c₄(t) (=I_d) and c₅(t) (=I_q).

The d-axis AC current I_d giving the desired value I_d* is given by the power balance condition at the PCC and a power transferred by the GSC to the PCC as below,

$\begin{array}{cc}\left(P_{\mathrm{dem}}-P_{\mathrm{ug}}\right)=\frac{3}{2}{V}_d{I}_d& \left(50\right)\end{array}$ where P_dem and P_ug represent a power demand at the load and a power exchanged between the PCC and the utility grid, respectively, where P_ug>0, when provided from the utility grid to the PCC, P_ug<0, when provided from the PCC side to the utility grid, V_d represent a d-axis AC voltage of the AC output from the GSC.

Thus, the desired values defined for the d-axis and the q-axis AC current are given respectively as,

$\begin{array}{cc}{c}_4^{*}\left(t\right)=I_d^{*}=I_d=\frac{2}{3}\frac{P_{\mathrm{dem}}-P_{\mathrm{ug}}}{V_d},& \left(51\right)\end{array}$ c ₅*(t)=I_q*=0,  (52)

The tracking errors e₄(t) for the d-axis AC current I_d and e₅(t) for the q-axis AC current I_q are given as:e₄(t)=I_d(t)−I_d*(t),  (53)e₅(t)=I_q(t)−I_q*(t)=I_q(t).  (54)

Differentiating the tracking error e₄(t) and e₅(t) gives:

$\begin{array}{cc}{\stackrel{.}{e}}_4\left(t\right)=\frac{1}{L_f}\left[V_d-R_f{I}_d-U_d+L_f{\omega }_g{I}_q\right]+H_4-{\stackrel{.}{I}}_d^{*}\left(t\right);& \left(55\right)\end{array}$ $\begin{array}{cc}{\dot{e}}_5\left(t\right)=\frac{1}{L_f}\left[V_{\mathrm{dq}}-R_f{I}_q-U_q-L_f{\omega }_g{I}_d\right]+H_5-{\stackrel{.}{I}}_q^{*}\left(t\right),& \left(56\right)\end{array}$ where H₄ and H₅ are the lumped disturbances in the HMS 100.

FOSM surfaces ζ₄(t) and ζ₅(t) for the characteristics elements c₄(t) (=I_d) and c₅(t) (=I_q) are defined respectively as:ζ₄(t)=k₄R_t^μe₄(t)+^R_t^1-μe₄(t);  (57)ζ₅(t)=k₅R_t^μe₅(t)+^RD_t^1-μe₅(t);  (58)where μ ∈ (0, 1), k₄, and k₅ are positive constants, ^R_t^μ and ^RD_t^1-μ each denotes a Riemann-Liouville fractional integral and a Riemann-Liouville fractional derivation, respectively.

Riemann-Liouville fractional derivatives of the FOSM surfaces ζ₄(t) and ζ₅(t) are given respectively as:^RD_t^μ₄(t)=k₄e₄(t)+ė₄(t);  (59)^RD_t^μ₅(t)=k₅e₅(t)+ė₅(t).  (60)

Substituting Eqs. (51) and (52) for ė₄ and ė₅ of the above equations respectively, gives

$$\begin{gathered} \begin{array}{cc}{ }_{}^R{𝒟}_t^{\mu }{\zeta }_4\left(t\right)=k_4{e}_4\left(t\right)-{\stackrel{.}{I}}_d^{*}+\frac{1}{L_f}\left[V_d-R_f{I}_d-U_d+L_f{\omega }_g{I}_q+H_4\right], \\ & \left(61\right)\end{array} \end{gathered}$$ $\begin{array}{cc}{ }_{}^R{𝒟}_t^{\mu }{\zeta }_5\left(t\right)=k_5{e}_5\left(t\right)-{\stackrel{.}{I}}_q^{*}+\frac{1}{L_f}\left[V_q-R_f{I}_q-U_q-L_f{\omega }_g{I}_d+H_5\right].& \left(62\right)\end{array}$

Here, the equivalent control law elements u₄^eqv(t) and u₅^eqv(t) of the characteristic elements c₄(=I_d) and c₅(=I_q) are derived from conditions giving ^RD_t^μζ_i(t)=0, (i=4, 5) in Eqs. (57) and (58), respectively. Referring the two equations, the equivalent control law elements u₄^eqv(t) and u₅^eqv(t) are selected as V_d and V_q, the d-axis and the q-axis AC voltages of the output of the GSC 108, respectively.

The equations for the equivalent control law elements u₄^eqv(t) and u₅^eqv(t) are obtained by further replacing H_i(i=4, 5) with ^R_t^1-μδ_i(i=4, 5), respectively, as follows:

$\begin{array}{cc}{u}_4^{\mathrm{eqv}}\left(t\right)=V_d^{\mathrm{eqv}}\left(t\right)=-L_f\left(\begin{array}{c}{k}_4{e}_4\left(t\right)-{\stackrel{.}{I}}_d^{*} \\ -\frac{1}{L_f}\left[R_f{I}_d+U_d-L_f{\omega }_g{I}_q{-}_{}^R{𝒥}_t^{1-\mu }{\delta }_4\right]\end{array}\right);& \left(63\right)\end{array}$ $\begin{array}{cc}{u}_5^{\mathrm{eqv}}\left(t\right)=V_q^{\mathrm{eqv}}\left(t\right)=-L_f\left(\begin{array}{c}{k}_5{e}_5\left(t\right)-{\stackrel{.}{I}}_q^{*} \\ -\frac{1}{L_f}\left[R_f{I}_q+U_q+L_f{\omega }_g{I}_d{-}_{}^R{𝒥}_t^{1-\mu }{\delta }_5\right]\end{array}\right),& \left(64\right)\end{array}$ where, the lumped disturbances H_i in the HMS, are assumed to be bounded and satisfy below conditions:∥H_i∥≤^R_t^1-μδ_i, and ∥^R_t^1-μH_i∥≤δ_i(i=4, 5)  (65)where ^R_t^1-μδ_i, (i=4, 5) represent external disturbances and parametric perturbations with δ_i is a positive function.

Now, the control law elements u₄^cnt(t) and u₅^cnt(t) of the characteristic elements c₄(I_d) and c₅(=I_q) are defined based on the equivalent control law elements Eqs. (59) and (60), respectively,

$\begin{array}{cc}\begin{array}{c}{u}_4^{\mathrm{cnt}}\left(t\right)=V_d^{\mathrm{cnt}}\left(t\right)\\ =V_d^{\mathrm{eqv}}\left(t\right)-L_f\left[{{\varrho }_4}_{}^R{𝒥}_t^{1-\mu }SG\left({\zeta }_4\left(t\right)\right){❘{\zeta }_4\left(t\right)❘}^{\alpha }+{{\gamma }_4}_{}^R{𝒥}_t^{1-\mu }{\zeta }_4\left(t\right)\right],\end{array}& \left(66\right)\end{array}$ $\begin{array}{cc}\begin{array}{c}{u}_5^{\mathrm{cnt}}\left(t\right)=V_q^{\mathrm{cnt}}\left(t\right)\\ =V_q^{\mathrm{eqv}}\left(t\right)-L_f\left[{{\varrho }_5}_{}^R{𝒥}_t^{1-\mu }SG\left({\zeta }_5\left(t\right)\right){❘{\zeta }_5\left(t\right)❘}^{\alpha }+{{\gamma }_5}_{}^R{𝒥}_t^{1-\mu }{\zeta }_5\left(t\right)\right],\end{array}& \left(67\right)\end{array}$ where, α ∈ (0, 1), _i and γ_i(i=4, 5) are positive constants, and SG (ζ_i(t)) (i=4, 5) represent signum function sgn(ζ_i(t)) and its smooth approximations, for example,

$\tanh \left(\frac{{\zeta }_i\left(c\right)}{\vartheta }\right)$ with ϑ>0.

As described above, the GSMCFO method applied to the GSC control of the HMS 100 includes calculations of the FOSM surfaces ζ₄(t) and ζ₅(t), the control law elements u₄^cnt(t) and u₅^cnt(t) including a fractional time integral and a fractional time differentiation of the tracking errors e₄ and e₅, respectively. The controller is configured to i) monitor a status of the HMS, including the characteristics elements c₄ and c₅, defined and specified in the definition set as the d-axis AC current I_d and the q-axis AC current I_q of the AC output of the GSC 108, respectively, ii) calculate the control law elements u₄^cnt(t)=V_d^cnt(t), and u₅^cnt(t)=V_q^cnt(t) defined with specific equations for the d-axis and the q-axis AC voltages of the output of the GSC 108, respectively, referring the definition set in the preinstalled software and based on monitored status of the HMS, and iii) control the GSC 108 by applying the control law elements u₄^cnt(t)=V_d^cnt(t), and u₅^cnt(t)=V_q^cnt(t) calculated, when the tracking errors are perceived. The control law elements guarantee the characteristic elements, the d-axis AC current I_d and the q-axis AC current I_q of the GSC 108 effectively arrive and stay on the FOSM surfaces ζ_i(t) (i=4, 5) in the presence of external disturbances, parametric perturbation, and unknown dynamics.

Applying the GSMCFO method and the control law elements u_i^cnt(t), (i=4, 5) by the controller 120 realizes a condition:

${\zeta }_i\left(t\right)\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}<0,\left(I=4,5\right)$ as far as ζ_i(t) is nonzero, as discussed earlier. Whenever the condition is satisfied, it is an indication that the characteristics element c_i (i=4, 5), the d-axis AC current I_d and the q-axis AC current I_q of the GSC 108 are approaching the FOSM surface ζ_i(t), (i=4, 5) and sliding along the FOSM surface toward a global stabilized point satisfying

${\zeta }_i\left(t\right)=\frac{d{\zeta }_i\left(t\right)}{\mathrm{dt}}=0,\left(i=4,5\right).$ Modeling of the BBBC with the Rechargeable Battery and Application of GSMCFO Method

The bidirectional buck/boost converter (BBBC) 112 is configured to connect the rechargeable battery (BESS) 114 with the DC-link 132 as illustrated in FIG. 1. The aim of the buck/boost control by the BBBC 112 is to facilitate efficient charging/discharging of the BESS to maintain the power balance of the HMS 100.

The BBBC 112 is configured to behave as a buck converter when charging the BESS 114 and as a boost converter when supplying the load 126 or discharging. In other words, when an excess power is generated compared to a power demand, the controller 120 is configured to store the excess power by recharging the BESS 114 via the BBBC 112 that behaves as a buck converter. On the other side, when the demand power is larger than a combined power from the WT, the PV system and the grid 130, the BESS 114 is configured to discharge via the BBBC 112 that behaves as a boost converter during discharging the BESS 114. Detailed mathematical model of the BBBC 112 and electrically connected BESS 114 is described below.

A dynamic model of the BESS 114 is given for charging operation by the BBBC as:

$\begin{array}{cc}{L}_b\frac{{\mathrm{dI}}_b}{\mathrm{dt}}=\left(V_b-I_b{R}_b-{\mathrm{dV}}_{dc}\right),& \left(68\right)\end{array}$ where L_b is the battery inductance, I_b is the battery current, V_b is the battery voltage, V_dc is the voltage of the DC-link 132, and D is the duty cycle of the BBBC 112. Dynamics of a state-of-charge (SOC) of the rechargeable battery is given by:

$\begin{array}{cc}\frac{d\left(\mathrm{SOC}\right)}{\mathrm{dt}}=-\frac{{\eta }_b}{Q_b}{I}_b,& \left(69\right)\end{array}$ where Q_b and η_b stand for the battery capacity and efficiency, respectively.

To maintain the efficiency, longevity and reliability, the SOC is constrained by the minimum SOC(SOC^min) and the maximum SOC(SOC^min) as provided below:SOC^min<SOC<SOC^min.  (70)

A desired battery current I_b* is determined based on the power balancing condition in the HMS 100, as below. The battery charging and discharging power P_b of the BESS 114 is controlled in accordance with a power balancing condition given as,P_re+P_ug=P_dem+P_b,  (71)where, P_re (≅0) is a sum of powers from renewable energy sources, P_ug, a power provided from the utility grid, and P_dem (≥0), a load demand by the load 126. In Eq. (67), the battery power P_b is positive when the BESS is charged. Thus, P_b and a desired value of the battery current I_b* are given respectively as,P_b=P_re+P_ug−P_dem  (72)

$\begin{array}{cc}{I}_b^{*}=\frac{P_b}{V_b}=\frac{P_{\mathrm{re}}+P_{\mathrm{ug}}-P_{\mathrm{dem}}}{V_b}.& \left(73\right)\end{array}$

When a battery current monitored was I_b (t) at time t monitored, a tracking error e₆ (t) is given by:e₆(t)I_b(t)−I_b  *(t). (74)

Taking a time derivation of the tracking error e₆ (t) and substituting Eq. (64) give,

$\begin{array}{cc}{\stackrel{.}{e}}_6=\frac{{\mathrm{dI}}_b}{\mathrm{dt}}-{\stackrel{.}{I}}_b^{*}\left(t\right)=\frac{1}{L_b}\left(V_b-I_b{R}_b-{\mathrm{DV}}_{dc}+H_6\right)-{\stackrel{.}{I}}_b^{*}\left(t\right),& \left(75\right)\end{array}$ where H₆ represents a lumped disturbance in the HMS 100.

A fractional-order sliding-mode (FOSM) surface ζ₆(t) is defined as:ζ₆(t)=k₆^R_t^μe₆+^R_t^1-μe₆,  (76)where μ ∈ (0, 1) and k₆ are positive constants, ^R_t^μ denotes a Riemann-Liouville fractional integration, ^R_t^1-μ denotes a Riemann-Liouville fractional derivation.

Riemann-Liouville fractional derivatives of the FOSM surfaces ζ₆(t) is given as:^R_t^μζ₆(t)=k₆e₆(t)+ė₆(t).  (77)

Substituting Eq. (71) for ė₆ of the above equations gives

$\begin{array}{cc}R{t}_{\mu }^{}{\zeta }_6\left(t\right)=k_6{e}_6\left(t\right)+\frac{1}{L_b}\left(V_b-I_b{R}_b-D{V}_{dc}+H_6\right)-{\stackrel{.}{I}}_b^{*}\left(t\right),& \left(78\right)\end{array}$

Here, the equivalent control law elements u₆^eqv(t) of the characteristic elements c₆ (=I_b) is derived from a condition giving ^R_t^μζ₆(t)=0 in Eq. (73). Referring Eq. (73), the equivalent control law elements u₆^eqv(t) is selected as the duty ratio D of the BBBC 112.

The equation for the equivalent control law elements u₆^eqv(t)=D^eqv(t) is obtained by further replacing H₆ with ^R_t^1−μδ₆:

$\begin{array}{cc}{u}_6^{eqv}\left(t\right)=D^{eqv}\left(t\right)=\frac{L_b}{V_{dc}}\left(K_6{e}_6\left(t\right)+\frac{1}{L_b}\left[V_b-I_b{R}_b+R{t}_{1-\mu }^{}{\delta }_6\right]-I_b^{*}\right),& \left(79\right)\end{array}$ where, the lumped disturbances H₆ in the HMS, is assumed to be bounded and satisfies below conditions:∥H₆∥≤^R_t^1−μδ₆, and ∥^R_t^1−μH₆∥≤δ₆,  (80)where ^R_t^1−μδ₆ represents external disturbances and parametric perturbations with δ₆ a positive function.

The control law element u₆^cnt(t)=D^cnt(t) is defined as:

$\begin{array}{cc}\begin{array}{c}{u}_6^{\mathrm{cnt}}\left(t\right)=D^{\mathrm{cnt}}\left(t\right)\\ =D^{eqv}\left(t\right)-L_f\left[{\varrho }_6{ }_{}^R{t}_{1-\mu }^{}\mathrm{SG}\left({\zeta }_6\left(t\right)\right){❘{\zeta }_6\left(t\right)❘}^{\alpha }+{\gamma }_6{ }_{}^R{t}_{1-\mu }^{}{\zeta }_6\left(t\right)\right],\end{array}& \left(81\right)\end{array}$ where, α ∈ (0, 1), ₆ and γ₆ are positive constants, and SG(ζ_i(t)) (i=6) represents a signum function sgn(ζ_i(t)) and its smooth approximations, for example,

$\tanh \left(\frac{{\zeta }_i\left(t\right)}{\vartheta }\right)$ with ϑ>0.

As described above, the GSMCFO method applied to the GSC control of the HMS 100 includes calculation of the FOSM surfaces ζ₆(t), the control law elements u₆^cnt(t) including a fractional time integral and a fractional time differentiation of the tracking errors e₆. The controller is configured to i) monitor a status of the HMS, including the characteristics element c₆ defined and specified in the definition set as the battery current I_b, ii) calculate the control law elements u₆^cnt(t)=D^cnt(t), defined with the specific equation for the duty ratio of the BBBC 112, referring the definition set in the preinstalled software and based on monitored status of the HMS, and iii) control the BBBC 112 by applying the control law element u₆^cnt(t)=D^cnt(t) calculated, when the tracking error is perceived. The control law element guarantees the characteristic element c₆, that is, the battery current I_b effectively arrive and stay on the FOSM surfaces ζ₆(t) in the presence of external disturbances, parametric perturbation, and unknown dynamics.

Applying the GSMCFO method and the control law elements u₆^cnt(t) by the controller 120 realizes a condition:

${\zeta }_6\left(t\right)\frac{d{\zeta }_6\left(t\right)}{\mathrm{dt}}<0,$ as far as ζ₆ (t) is nonzero, as discussed earlier. Whenever the condition is satisfied, it is an indication that the characteristics element c₆, that is, the battery current I_b is approaching the FOSM surface ζ₆(t) and sliding along the FOSM surface toward a global stabilized point satisfying

${\zeta }_6\left(t\right)=\frac{d{\zeta }_6\left(t\right)}{\mathrm{dt}}=0.$

The control law element guarantees I_b to effectively arrive and stay on the sliding mode surface ζ₆ (t) in the presence of external disturbances, parametric perturbation, and unknown dynamics.

In some implementations according to certain embodiments, the signum functions SG(ζ_i(t)) in the control law elements u_i^cnt(t) are selected as hyperbolic tangent functions tanh (ζ_i/θ), where θ (>0) is a constant. This is effective to avoid a chattering that may be caused due to a high frequency switching of the discontinuous function sgn(ζ_i(i)). In such case, the modified control law elements would be as below:

$\begin{array}{cc}{V}_{qs}=V_{\mathrm{qs\_eqv}}-\frac{1}{a}\left[{\varrho }_1{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_1}{\vartheta }\right){❘{\zeta }_1\left(t\right)❘}^{\alpha }+{\gamma }_1{ }_{}^R{t}_{1-\mu }^{}{\zeta }_1\left(t\right)\right],& \left(82\right)\end{array}$ $\begin{array}{cc}{V}_{ds}=V_{\mathrm{ds\_eqv}}-L_d\left[{\varrho }_2{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_2}{\vartheta }\right){❘{\zeta }_2\left(t\right)❘}^{\alpha }+{\gamma }_2{ }_{}^R{t}_{1-\mu }^{}{\zeta }_2\left(t\right)\right],& \left(83\right)\end{array}$ $\begin{array}{cc}{I}_d=I_{\mathrm{d\_eqv}}+\frac{2}{3}\frac{C_{dc}{V}_{dc}}{V_d}\left[{\varrho }_3{ }_{}{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_3}{\vartheta }\right){❘{\zeta }_3\left(t\right)❘}^{\alpha }+{\gamma }_3{ }_{}{t}_{1-\mu }^{}{\zeta }_3\left(t\right)\right],& \left(84\right)\end{array}$ $\begin{array}{cc}{V}_d=V_{\mathrm{d\_eqv}}-L_f\left[{\varrho }_4{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_4}{\vartheta }\right){❘{\zeta }_4\left(t\right)❘}^{\alpha }+{\gamma }_4{ }_{}^R{t}_{1-\mu }^{}{\zeta }_4\left(t\right)\right],& \left(85\right)\end{array}$ $\begin{array}{cc}{V}_q=V_{\mathrm{q\_eqv}}-L_f\left[{\varrho }_5{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_5}{\vartheta }\right){❘{\zeta }_5\left(t\right)❘}^{\alpha }+{\gamma }_5{ }_{}^R{t}_{1-{\mu }^1}^{}{\zeta }_5\left(t\right)\right],& \left(86\right)\end{array}$ $\begin{array}{cc}D=D_{eqv}-L_b\left[{\varrho }_6{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_6}{\vartheta }\right){❘{\zeta }_6\left(t\right)❘}^{\alpha }+{\gamma }_6{ }_{}^R{t}_{1-\mu }^{}{\zeta }_6\left(t\right)\right].& \left(87\right)\end{array}$

Here, a mathematical proof is described on a point that the GSMCFO method described above guarantees all states' trajectories converge to the sliding-mode surfaces ζ_i=0, (i=1,2, . . . , 6) within a finite time.

A Lyapunov function candidate is selected as:

$\begin{array}{cc}L=\frac{{\zeta }_1^2\left(t\right)}{2}+\frac{{\zeta }_2^2\left(t\right)}{2}+\frac{{\zeta }_3^2\left(t\right)}{2}+\frac{{\zeta }_4^2\left(t\right)}{2}+\frac{{\zeta }_5^2\left(t\right)}{2}+\frac{{\zeta }_6^2\left(t\right)}{2}.& \left(88\right)\end{array}$

Differentiating L with respect to time gives:{dot over (L)}=Σ_i=1⁶ζ_i(t){dot over (ζ)}_i(t)=Σ_i=1⁶ζ_i(t)^R_t^1−μ(R_t^μζ_i(t)).  (89)Substituting (77)-(82) into (84), one obtains:

$\begin{array}{cc}\begin{array}{c}\dot{L}={\sum }_{i=1}^6{\zeta }_i\left(t\right){ }_{}^R{t}_{1-\mu }^{}\left(\begin{array}{c}\frac{1}{L_f}\left(H_i-{ }_{}^R{t}_{1-\mu }^{}{\delta }_i\right)\\ -{\varrho }_i{ }_{}^R{t}_{1-\mu }^{}\tanh \left(\frac{{\zeta }_i}{\vartheta }\right){❘{\zeta }_i\left(t\right)❘}^{\alpha }-{\gamma }_i{ }_{}^R{t}_{1-\mu }^{}{\zeta }_i\left(t\right)\end{array}\right)\\ ={\sum }_{i=1}^6{\zeta }_i\left(\begin{array}{c}\frac{1}{L_f}\left({ }_{}^R{t}_{1-\mu }^{}{H}_i-{\delta }_i\right)\\ -{\varrho }_i\tanh \left(\frac{{\zeta }_i}{\vartheta }\right){❘{\zeta }_i\left(t\right)❘}^{\alpha }-{\gamma }_i{\zeta }_i\left(t\right)\end{array}\right)\\ ={\sum }_{i=1}^6\left(\begin{array}{c}\frac{1}{L_f}{\zeta }_i\left({ }_{}^R{t}_{1-\mu }^{}{H}_i-{\delta }_i\right)\\ -{\varrho }_i\tanh \left(\frac{{\zeta }_i}{\vartheta }\right){❘{\zeta }_i\left(t\right)❘}^{\alpha +1}-{\gamma }_i{\zeta }_i^2\left(t\right)\end{array}\right).\end{array}& \left(90\right)\end{array}$ Based on assumptions, ∥^R_t^1−μH_i∥≤δ_i(i=1,6) we have:

$\begin{array}{cc}\dot{L}\le -{\sum }_{i=1}^6{\rho }_i❘\tanh \left(\frac{{\zeta }_i}{\vartheta }\right)❘{❘{\zeta }_i\left(t\right)❘}^{\alpha +1}-{\sum }_{i=1}^6{\gamma }_i{❘{\zeta }_i\left(t\right)❘}^2;& \left(91\right)\end{array}$ Therefore, we obtain:{dot over (L)}=E_i=1⁶ζ_i(t)ζ_i(t)≤−Σ_i=1⁶_i∥ζ_i(t)∥₂^α+1−Σ_i=1⁶γ_i|ζ_i(t)|²≤0.  (92)Consequently, it can be concluded that all the states' trajectories will converge to their respective sliding-mode surfaces ζ_i=0, (i=1, 2, . . . , 6).

Further a convergence time of the states' trajectories is finite as described below. From (87) and with a definition of norm,

${{\zeta }_i\left(t\right)}_2={\left[{\sum }_{i=1}^6{❘{\zeta }_i\left(t\right)❘}^2\right]}^{\frac{1}{2}}={\left(2L\right)}^{\frac{1}{2}},$ one has:

$\begin{array}{cc}\frac{dL}{\mathrm{dt}}\le -{{\varrho }_i\left(2L\right)}^{\frac{\alpha +1}{2}}-{\gamma }_i\left(2L\right);& \left(93\right)\end{array}$ Rearranging (88) yields:

$\begin{array}{cc}\begin{array}{c}\mathrm{dt}\le -\frac{dL}{{{\varrho }_i\left(2L\right)}^{\frac{\alpha +1}{2}}+{\gamma }_i\left(2L\right)}=-\frac{1}{2}\frac{{\left(2L\right)}^{-\frac{1}{2}}d\left(2L\right)}{{{\varrho }_i\left(2L\right)}^{\frac{\alpha }{2}}+{{\gamma }_i\left(2L\right)}^{\frac{1}{2}}}\\ \le -\frac{{d\left(2L\right)}^{\frac{1}{2}}}{{{\varrho }_i\left(2L\right)}^{\frac{\alpha }{2}}+{{\gamma }_i\left(2L\right)}^{\frac{1}{2}}}=-\frac{d{{\zeta }_i\left(t\right)}_2}{{\varrho }_i{{\zeta }_i\left(t\right)}_2^{\alpha }+{\gamma }_i{{\zeta }_i\left(t\right)}_2}\\ \le -\frac{{{\zeta }_i\left(t\right)}_2^{-\alpha }d{{\zeta }_i\left(t\right)}_2}{{\varrho }_i+{\gamma }_i{{\zeta }_i\left(f\right)}_2^{1-\alpha }}\\ \le -\frac{1}{{\gamma }_i\left(1-\alpha \right)}\frac{d\left({\gamma }_i{{\zeta }_i\left(t\right)}_2^{1-\alpha }\right)}{{\varrho }_i+{\gamma }_i{{\zeta }_i\left(t\right)}_2^{1-\alpha }}.\end{array}& \left(94\right)\end{array}$ Integrating both sides of (89) from zero to t_r yields:

$\begin{array}{cc}\begin{array}{c}{t}_r-0\le -\frac{1}{{\gamma }_i\left(1-\alpha \right)}{\int }_0^{t_r}\frac{d\left({\gamma }_i{{\zeta }_i\left(t\right)}_2^{1-\alpha }\right)}{{\rho }_i+{\gamma }_i{{\zeta }_i\left(t\right)}_2^{1-\alpha }}\\ {\le -\frac{1}{{\gamma }_i\left(1-\alpha \right)}\ln \left({\varrho }_i+{\gamma }_i{{\zeta }_i\left(t\right)}_2^{1-\alpha }\right)❘}_0^{t_r}\\ \le \frac{1}{{\gamma }_i\left(1-\alpha \right)}\ln \left(1+\frac{{\gamma }_i}{{\varrho }_i}{{\zeta }_i\left(0\right)}_2^{1-\alpha }\right).\end{array}& \left(95\right)\end{array}$

Eq. (90) indicates that the states' trajectories arrive at their respective sliding manifolds ζ_i=0, (i=1, 2, . . . ,6) within a finite time T_r given by:

$\begin{array}{cc}{T}_r=t_r-0\le \frac{1}{{\gamma }_i\left(1-\alpha \right)}\ln \left(1+\frac{{\gamma }_i}{{\varrho }_i}{{\zeta }_i\left(0\right)}_2^{1-\alpha }\right).& \left(96\right)\end{array}$ Power Balancing Operations of the HMS

As noted above, the controller of the HMS 100 is configured to satisfy the power balance condition given by Eq. (67),P_re+P_ug=P_dem+P_b,  (97)where, P_re (≥0) is a sum of powers from renewable energy sources, P_ug, a power provided from the utility grid, P_dem(≥0), a load demand, and a battery power P_b.As a measure for the power balance, a power imbalance ΔP of the HMS 100 is defined as,ΔP=P_re+P_ug−P_dem−P_b.  (98)Thus, the controller is configured to monitor each on the components of the power imbalance and to realize and maintain the balancing condition, alternatively given as,ΔP=0.  (99)

Here, the power P_re from the renewable energy sources and the load demand P_dem are deemed not adjustable in ordinary cases. The WT generator and the PV system are operated under the MPPT modes in normal situations. The load demand P_dem is also assumed non-negotiable. On the other hand, the battery power P_b and the grid power P_ug are assumed adjustable in both directions, that is, either providing to the load to offset a shortage, or receiving an excess from the HMS.

The controller of the HMS is configured to monitor each of the components of the power imbalance equation and a value of the power imbalance ΔP. When the power P_re from the renewable energy sources fluctuates as conventionally occurs, the controller is configured to realize and maintain the power balance condition ΔP=0, by adjusting the battery power P_b and the grid power P_ug.

In controlling the battery power, the controller is configured to satisfy a restriction condition on a state of charge (SOC) of the BESS for a reliability purpose, given as,SOC^min≤SOC≤SOC^max  (100)

When the SOC reaches the maximum value while maintaining the power balance condition by providing the P_b to the BESS, a power imbalance ΔP is caused when the charging is stopped to be P_b=0. In that instance, the imbalance, that is, an excess energy can be absorbed by the grid power P_ug. When the utility grid was supplying a larger amount than the excess energy ΔP, the grid power is reduced to absorb the excess energy. The utility grid can receive the excess energy without supplying the grid power (P_ug<0). Some examples for the power balancing operations of the controller are described below.

Case 1: When the power of the PV system 110 is decreased whereas the power due to the WT 102 is constant.

Suppose initially the PV system 110 is generating the 100 KW of power, the WT generator 102 is generating 70 KW of power and the grid 130 is providing 200 KW of power at a load demand at 350 KW, where the battery power is zero.

The power imbalance is calculatedΔP=P_re+P_ug−P_dem−P_b=170+200−350−0=20(KW)

Hence, the surplus 20 KW is used to charge the BESS 114 via the BBBC 112.

Accordingly, the controller 120 signals the BBBC 112 to start operating in the buck mode. (P_b=20 KW) By this operation, the imbalance is reduced to zero. (ΔP=0)

Suppose the PV system 110 is now producing smaller amount of power, for example only 80 KW for 10 seconds. Decrease in the power may be due to cloudy environment. During this period, the power imbalance turns to ΔP=−20 KW. At this initial instance, the controller immediately stops the battery power 20 KW providing to charge the BESS via the BBBC.

After 10^th second, when the PV system 110 again starts generating 100 KW of power, the controller 120 again signals the BBBC 112 to start charging the BESS at 20 KW in the boost mode. (P_b=20 KW)

Case 2: When the power of the PV system 110 is decreased whereas the power due to the WT generator 102 is constant and the grid 130 injects power

Suppose initially same situation as the Case 1; ΔP=P_re+P_ug−P_dem−P_b=170+200−350−0=20 (KW), the power imbalance ΔP=20 (KW) is balanced by charging the BESS 114 via the BBBC 112. (P_b=20 KW) Now, suppose the PV system 110 reduces its generation to a half (50 KW) for 10 seconds. Decrease in the power may be due to cloudy environment. The reduction causes the power imbalance of ΔP=−50 KW.

At this time, the controller immediately signals the BBBC 112 to stop charging (P_b: 20 KW to 0) and start discharging the BESS 114 P_b=−30 KW to realize the power balance condition. (ΔP=−50+20+30=0)

Suppose further, the SOC of the BESS 114 reaches to the SOC^min within 7 seconds, the controller 120 signals the BBBC 112 to stop discharging. At this moment, the controller 120 signals the grid 130 to start supplying an additional 30 KW, a total of 230 KW power to the load in the HMS 100 to meet the power balance requirement. The grid 130 starts supplying the power.

After 10^th second the PV system 110 again starts generating 100 KW of power. At this moment, the controller signals the grid 130 to stop injecting the additional 30 KW of power to the HMS 100. Also, the controller 120 again signals the BBBC 112 to start charging the BESS at 20 KW.

Case 3: The SOC of the BESS reaches to the maximum value SOC^max, while the power of the PV system 110 as well as one of the WT generator 102 are unchanged, then the grid absorbs the excess power

Suppose initially the same situation as of the Case 1; ΔP=P_re+P_ug−P_dem−P_b−170+200−350−0=20 (KW), the power imbalance ΔP=20 (KW) is balanced by charging the BESS 114 via the BBBC 112. (P_b=20 KW).

Suppose then at the 11^th second, the BESS reaches the condition SOC=SOC_max. Then, the controller immediately stops charging the BESS, and signals the grid 130 to start absorbing the excess power. Accordingly, the grid 130 starts absorbing the excess power, that is, 20 KW, thus reduces the grid power from 200 KW to 180 KW. The power is balanced in the HMS 100.

Tracking Performances: Simulation Results

Tracking performances of the HMS 100 controlled by the GSMCFO method under circumstance changes are described below. The configuration of the HMS 100 above described was implemented in MATLAB/SIMULINK. Design parameters and their values assumed for components including the WT 102, the PMSG 104, the PV system 110, the BESS 114 and the grid 130 are summarized in Tables 1 and 2. Control parameters for the GSMCFO-method are provided in Table 3. Simulation results obtained for four scenarios are described.

TABLE 1
Parameters of the renewable generation system.
Wind Turbine Generator	Solar PV Generator
Parameter	Symbol	Value	Parameter	Symbol	Value
Air density	ρ	1.25	Ambient	T	25
(kg/m3)			temperature
			(C.)
Radius of	R	28.2
wind
turbine
(m)
d-axis	Lds	9.8	p-n	A	1.8
stator			junction
current			factor
(mH)
q-axis	Lqs	9.8	Equivalent	Rsh	313.33
stator			shunt
current			resistor (Ω)
(mH)
Rotor flux	Λr	28	Equivalent	Rse	0.193
(Wb)			series
			resistor (Ω)
Inertia of	J	4000	Short-	Isc	2.8
mechanical			circuit
shaft			current (A)
(Kg · m2)
Number of	P	8	Number of	Ns	68
pole pairs			parallel
			modules
Optimum	Λopt	8.1	Number of	Np	95
tip speed			series
ratio			modules
Power	Cpmax	0.48	Open-	Voc	32.9
coefficient			circuit
			voltage (V)
Rated	Pw	2	Rated PV	Ppv	1
wind			power
generator			(MW)
power			voltage (V)
(MW)

TABLE 2
Grid and energy storage parameters
Battery Energy	Grid
Storage System	Parameter
Parameter	Symbol	Value	Parameter	Symbol	Value
Battery	ηb	0.9	Filter	Lf	16.9
efficiency			inductance (mH)
Battery	Qb	1000	Line	Lg	1.69
capacity (AH)			inductance (mH)
Rated battery	Pb	1	Load	Pl	2
power (MW)			demand (MW)
Battery	Vb	550	Line-to-line	Ull	1000
voltage (V)			voltage (V)
Upper SOC	SOCmax	90	DC-bus	Cdc	1670
limit (%)			capacitance (μF)
Lower SOC	SOCmin	20
limit (%)

TABLE 3
Control system parameters.
	Parameters	Values	Parameters	Values
	k1	0.0117	k4	0.0847
	k2	0.0240	k5	0.0106
	k3	0.1342	k6	0.2302
	1	1.3239	4	5.6694
	2	0.7009	5	2.0982
	3	11.3652	6	15.7501
	γ1	12.3794	γ4	25.0214
	γ2	9.0402	γ5	13.0005
	γ3	19.7391	γ6	37.4478
	α	0.4	μ	0.8

Scenario 1: Power Generation from Solar PV Only

Under Scenario 1, the wind speed is assumed below the cut-in speed and the wind power is not large enough to overcome the mechanical resistance of the wind turbine. On the other hand, irradiance level varies depending on time. Therefore, the renewable power is generated by solar PV alone.

FIG. 2A is an exemplary graph illustrating a variation pattern in ambient irradiance level in the Scenario 1 for the HMS 100 according to certain embodiments. Here, the wind speed is not illustrated, since it is assumed below the cut-in speed. The irradiance level drops from 1000 W/m² to 940 W/m² at t=10 s, due to an intermittent conditions such as cloudy environment, then increases to 1000 W/m² at t=20 s, respectively. The controller 120 thus set the MPPT voltage of the PV system 110 as the desired voltage V_dc* of the DC-link 132. The total power is only due to the grid 130, BESS 114 and the PV system 110. The controller 120 thus signals the BBBC 112 to start working in the boost mode to support/maintain the power balance in the HMS 100. Therefore, the power of the BESS 114 rises exponentially to a certain level and stays there, for example 0.7 W in FIG. 2D for t<=10.

FIG. 2B is an exemplary graph illustrating variations of the DC-link voltages of the HMS 100 in the Scenario 1, according to certain embodiments. A curve 206 represents a desired value (“reference”) for the DC-link voltage (V_dc*), whereas a curve 208 represents a real time value of DC-link voltage V_dc. The DC-link voltage V_dc decreased for the time interval 10≤t(s)≤20 as the irradiance level decreased at the same interval as described in FIG. 2A. A transient time of the DC-link voltage V_dc observed was less than one second, where no transient overshoot was observed. Those demonstrate a promising tracking performance of the controller 120.

FIG. 2C is an exemplary graph illustrating an output power 210 from the PV system 110 of the HMS 100 in the Scenario 1 according to certain embodiments. The output power 210 from the PV system decreases for the time interval 10≤t(s)≤20, corresponding to the change of irradiance in FIG. 2A.

FIG. 2D is an exemplary graph illustrating an output power from the BESS 114 of the HMS 100 according to certain embodiments in the Scenario 1. The curve 212 indicates that during the time interval 10≤t(s)≤20, the BESS 114 discharges a more power than its previous power level to balance the load. After this duration, when the output power from the PV system 110 recovers as in FIG. 2C, the BESS 114 reduces the output power to the DC-link 132 at t=20 s as it was operating before t≤10 s. The curve 212 further indicates a sudden decrease at around t=26 s. This is due to a reduction of the SOC of the BESS 114 down to the SOC^Min as illustrated in FIG. 2H. At that moment, the BBBC 112 is configured to stop discharging from the BESS 114. The BESS 114 thus stops discharging to the DC-link 132 as it is forbidden to discharge below the SOC^Min.

FIG. 2E is an exemplary graph illustrating a grid power 214 supplied from the utility grid to the load 126 via the PCC and a power demand 216 at the load 126 of the HMS 100 according to certain embodiments in the Scenario 1. At the same moment that the BESS 114 stops discharging to the DC-link 132, the grid 130 is configured to start sending the grid power 214 to the load 126, in order to support the power balance of the HMS 100. The power demand 216 at the load 126 was assumed to be in a constant level.

FIG. 2F is an exemplary graph illustrating a power deviation Δ P 218 of the HMS 100 according to certain embodiments in the Scenario 1. The controller 120 is configured to immediately switch a power sources to a different type of source when required to balance the power in the HMS 100. The power deviation Δ P is configured to reduce to zero with a short time transient deviation. It is confirmed from FIG. 2F that the power deviation Δ P 218 due to switching power sources is actually maintained zero with minor transient deviations which vanishes in a short time. The transient deviations observed were with peak values less than 0.04 MW, which is less than about 3% of a sum of switched powers, with no overshoot and a transient time less than about one second.

FIG. 2G is an exemplary graph illustrating the battery current provided from the BESS 114 to the DC-link 132 of the HMS according to certain embodiments in the Scenario 1. The curve 220 represents the actual value and the curve 222 represents the desired value for the battery current discharging from the BESS 114. It is confirmed the actual battery current tracks the desired value exactly with a transient time less than one second and without an overshoot.

FIG. 2H is an exemplary graph illustrating a state of charge (SOC) of the BESS 114 of the HMS 100, according to certain embodiments, in the Scenario 1. During a time period 0≤t (s)≤26, the SOC continues decreasing due to the power supply from the BESS 114 to the load 126 for the power balance of the HMS 100, and at the time t=26 s, the SOC reaches to the SOC^Min, then the power supply from the BESS is stopped, as illustrated in FIG. 2D. For a time period of 26≤t(s)≤40, the SOC stays at its minimum value of 20%, since the BESS has stopped discharging.

Scenario 2: Power Generation from Wind Only

FIG. 3A is an exemplary graph illustrating a variation pattern of wind speed at the HMS 100 according to certain embodiments in Scenario 2. At night, the PV system 110 does not produce electricity, whereas the wind often blows above the cut in speed. In the Scenario 2, the wind power is the only renewable energy source supplying power to the load 126 with a constant demand. As illustrated in FIG. 3A, the wind speed is assumed to rise from 9 m/s to 10 m/s, further to 11 m/s, and then to decrease to 9.5 m/s at t=10 s, 20 s, and at 30 s, respectively. Thus, a variation range of about ±10% was assumed for the wind speed.

FIG. 3B is an exemplary graph illustrating the rotor speed of the electric generator (PMSG) in the HMS 100 according to certain embodiments in the Scenario 2. The solid line 302 represents a desired value of the rotor speed, whereas the dashed line 304, an observed rotor speed. It is confirmed that both of the solid line 302 and the dashed lines 304 track the wind speed variation with stability, with no transient time for the desired value 302 and with transient times of the observed rotor speed 304 less than a second, but without any overshoot. FIG. 3B demonstrates a promising tracking performance of the controller of the HMS observed for the rotor speed when the wind speed varies.

FIG. 3C is an exemplary graph illustrating the DC-link voltages V_dc of the HMS 100 according to certain embodiments in the Scenario 2. The solid line 306 represents the desired value (“reference”) voltage V_dc* of the DC-link 132, whereas the dashed line 308, the observed voltage V_dc of the DC-link 132. Despite the ±10% variation of the wind speed, the observed voltage V_dc of the DC-link 132 is regulated to the desired value (“reference”) voltage V_dc*. Transition noises peaks are observed in the observed voltage V_dc, with peak values less than about 10% of the V_dc, and half-widths less than one second, transients.

FIG. 3D is an exemplary graph illustrating a power generated from the WT generator 102 and 104 of the HMS 100 according to certain embodiments, in the Scenario 2. Solid line 310 represents wind associated power (“Wind power”) generated from the WT 102 and the PMSG 104. FIG. 3D demonstrates that the Wind power tracks the variation of the wind speed in real time with negligible delay.

FIG. 3E is an exemplary graph illustrating a power generated from or provided to the BESS 114 of the HMS 100 according to certain embodiments in the Scenario 2. Here, a power flow direction discharging from the BESS is taken as positive in FIG. 3E. FIG. 3E demonstrates that the BESS 114 is configured to fill in gaps when a Wind power is smaller than a power demand at the load 126 by discharging a power from the BESS, and also when a Wind power exceeds the power demand by receiving a redundant amount for charging the BESS. FIG. 3E also demonstrates that transitions between different discharging/charging powers of the BESS occurs with transient time less than a second and without any overshoot.

FIG. 3F is an exemplary graph illustrating a power demand at the load (“load demand”) and the grid power in the HMS in the Scenario 2, according to certain embodiments. Dashed line 314 represents the load demand, solid line 316, the power supplied to the load 126 from the grid 130. The dashed line 314 indicates the power demand at the load 126 remained constant. The solid line 316 indicates the gird 130 exchanged no power between the PCC 128, since the BESS 114 could fill in the gaps within its allowable SOC range, as discussed later in FIG. 3I.

FIG. 3G is an exemplary illustration of a power deviation of the HMS 100, according to certain embodiments in the Scenario 2. The power deviation (ΔP) remained zero except for transient peaks observed when a transition of the wind speed and hence transition of the Battery power and the Wind power occurred. Peak values of the transient peaks were with less than about 0.02 MW, corresponding to less than about 5% of power transition amount in the Wind power or in the Battery power, while half widths of the transition peaks were enough shorter than a second.

FIG. 3H is an exemplary illustration of a battery current of the BESS 114 in the HMS 100 according to certain embodiments in the Scenario 2. Solid line 320 represents the desired value (“reference”) for the current through the BESS 114, dashed line 322, observed current through the BESS 114. It is confirmed that the observed battery current 322 tracks closely the desired value 320 with transient time less than a second.

FIG. 3I is an exemplary illustration of the SOC of the BESS 114 in the HMS 110, according to certain embodiments in the Scenario 2. It is confirmed that the SOC 324 decreases whenever the BESS 114 supplies the battery power to the load 126, whereas the SOC increases whenever the BESS 114 is constantly receiving power from the other sources of the HMS.

Scenario 3: Power Generation from Both Wind and PV System

Here, complementary property of the solar and wind power sources was examined. FIG. 4A is an exemplary graph illustrating a solar irradiance level and a wind speed at the HMS according to certain embodiments assumed in the Scenario 3.

Solid line 402 and dashed line 404 each represents a solar irradiance level (in kW/m²) and a wind speed (in 10 m/s), respectively. For a time period 0≤t(s)≤5, the solar irradiance level 402 is still high, and the wind speed is above cut-off. From t=5 s, the solar irradiance starts decreasing. While, the wind speed starts increasing from t=10 s.

FIG. 4B is an exemplary graph illustrating the DC-link voltages of the HMS 100, according to certain embodiments in the Scenario 3. Dotted line 406 represents an observed value V_dc of the DC-link voltage, whereas solid line 408, a desired value (“reference”) of the DC-link voltage V_dc*. Since the MPPT voltage of the PV system V_pv^MPPT is not below the minimum allowable voltage of the DC-link 132 as for the solar irradiance level 402 in FIG. 4A, the MPPT voltage V_pv^MPPT was set as the desired “reference” voltage of the DC-link voltage V_dc* 408. FIG. 4B demonstrates that the observed value of the DC-link voltage V_dc tracks exactly the “reference” value V_dc*=V_PV^MPPT except for negligible transient peaks observed at power transitions, for example at t=5, 15 and 30 s. The transient peaks are less than about 10% of V_dc in peak values and diminish in a shorter time than a second.

FIG. 4C is an exemplary graph illustrating rotor speeds (angular velocity) of the WT generator of the HMS according to certain embodiments in the Scenario 3. Solid line 410 and dashed line 412 each represents a desired (reference) value of the rotor speed ω_r* and an observed rotor speed ω_r, respectively. FIG. 4C demonstrates that the observed rotor speed 412 exactly tracks the reference value 410, with negligible transition delays shorter than a second.

FIG. 4D is an exemplary graph illustrating powers generated by the WT generator and by the solar PV system of the HMS 100 according to certain embodiments in the Scenario 3. Solid line 414 and dotted line 416 each represents a PV power generated by the PV system 110 and a Wind power generated by the WT 102/PMSG 104, respectively. FIG. 4D confirmed that both the PV system 110 and the WT 102/PMSG 104 tracks variations of the ambient irradiance and the wind speed without observable delay time.

FIG. 4E is an exemplary graph illustrating a load demand 418 and a grid power 420 in the HMS 100 according to certain embodiment in the Scenario 3. Dotted line 418 and solid line 420 each represents the load demand and the grid power, that is, a power supplied (>0) from or received (<0) at the grid 130, respectively. The load demand 418 was assumed to increase at time t (s)=20, from 1 MW to 1.5 MW. However, at that moment the PV system 110 and the WT/PMSG 102/104 were both generating powers larger in total than the load demand, as observed in FIG. 4D. Thus, they could meet the load demand. At this moment excess energy was generated and stored to the BESS 114, as discussed in FIG. 4F. At time t (s)=32, the grid power 420 shifted to a minus level. This indicates the grid started to receive an excess power, because the BESS was fully charged up and stopped charging as discussed later in FIG. 4I.

FIG. 4F is an exemplary graph illustrating a battery power 422 generated from or provided to the BESS 114 of the HMS 100 according to certain embodiments in the Scenario 3.

A variation of the solid line 422 and negative value of the power indicates the BBBC 112 operated in the buck mode to charge an excess power the load demand. When an excess power is generated, the BESS 114 is configured to store the excess power, so far as the SOC of the BESS allows charging. On the other hand, when the SOC reached its maximum value SOC_max at t(s)=32.3, the BESS 114 stopped charging and the grid 130 started to absorb the excess power as illustrated in FIG. 4D with the curve 420, and in FIG. 4I discussed later.

FIG. 4G is an exemplary graph illustrating a power deviation 424 in the HMS 100 according to certain embodiment in the Scenario 3. Solid line 424 indicates that the power deviation Δ P is maintained exactly at zero under steady state conditions, while minor transition spikes were observed with peak values less than 5% of switched power values and transient times less than a second. FIGS. 4G and 4E demonstrated that the controller 120 is configured to switch the battery power immediately to a stable new level with acceptable minor transition spikes.

FIG. 4H is an exemplary graph illustrating a battery current of the BESS 114 in the HMS 100 according to an embodiment in the Scenario 3. Dashed line 426 and solid line 428 each represents an observed battery current and a desired (reference) value of the battery current of the BESS 114. FIG. 4H demonstrates that the observed battery current 426 exactly tracks the desired value 428 with minor transient delay less than a second and without transient spikes.

FIG. 4I is an exemplary graph illustrating a state of charge (SOC) 430 of BESS 114 of the HMS 100 according to an embodiment, in the Scenario 3. Observed variation of the SOC 430 demonstrates that the BESS 114 continues to receive an excess energy and increase the SOC so far as the excess energy is generated in the HMS and the SOC level allows further charging.

Scenario 4: Comparison with Proportional-Integral (PI) Controller

Here, performances of the HMS 100 with the controller 120 are compared with those of a HMS with a linear PI controller in the known art. The superiority the controller 120 is demonstrated. For performing the comparison, the GSMCFO controller 120 in the HMS 100 under certain embodiments was replaced, at the same parameters, with the PI controller utilized in a following reference: Chishti, F. et al, LMMN-Based Adaptive Control for Power Quality Improvement of Grid Intertie Wind-PV System. IEEE Trans. Ind. Inform. 2019, 15, 4900-4912, entire content is incorporated herein by reference. Same power variations as illustrated FIG. 4A in the Scenario 3 were assumed for the solar irradiance and the wind speed. A parametric uncertainty of ±50% and constant disturbances were incorporated in the conditions of the Scenario 4.

FIG. 5A is an exemplary graph illustrating rotor speeds observed in the HMS 100 with GSMCFO controller 120 under certain embodiments and observed with the PI controller for comparison in the Scenario 4. Solid line 504 represents a desired value (reference) of the rotor speed ω_r*. Dashed line 506 represents an observed rotor speed for the HMS with the GSMCFO controller 120, where the exact tracking performance was demonstrated again with minor transition delays less than a second, but without any overshoot.

Dashed-two dotted line 502 represents an observed rotor speed for the HMS with the PI controller, where overshoots and damping oscillations were observed. Peak values of the overshoots exhibited about 30 to 50% larger values than the reference values, while the damping oscillation continued for about three seconds.

FIG. 5A demonstrates that the GSMCFO controller 120 tracks the reference of the rotor speed more exactly and settles faster in transitions than the PI controller does.

FIG. 5B is an exemplary graph illustrating DC-link voltages observed for the HMS 100 with GSMCFO controller 120 under certain embodiments and observed with the PI controller in the Scenario 4 in comparison. Solid line 510 represents a desired value (reference) of the DC-link voltage V_dc*. Dashed line 512 represents an observed DC-link voltage for the HMS with the GSMCFO Controller 120 which demonstrated an exact tracking performance with minor transition delays less than a second and without any overshoot. Dashed-two dotted line 508 represents an observed DC-link voltage for the HMS with the PI controller which exhibited transition delays longer than a second, overshoots reaching about 20 to 30% larger or smaller V_dc values than the reference values, and overshoot damping continuing more than three seconds. FIG. 5B demonstrates that the GSMCFO controller tracks the DC-link voltage more exactly and settles faster in transitions than the PI controller does.

FIG. 5C is an exemplary graph illustrating battery currents observed in the HMS 100 with GSMCFO controller 120 under certain embodiments and observed with the PI controller in the Scenario 4 in comparison.

Solid line 516 represents a desired value (reference) of the battery current I_b*. Dashed line 518 represents an observed battery current for the HMS with the GSMCFO Controller 120. The dashed line 518 demonstrated the exact tracking performance again with minor transition delays less than a second and without overshoots at transitions. Dashed-two dotted line 514 represents an observed battery current for the HMS with the PI controller. Here, the dashed-two dotted line 514 exhibited transition delays longer than a second, overshoots reaching about 10% larger or smaller I_b values than the reference, and overshoot damping continuing more than a few seconds. FIG. 5C also demonstrates that the GSMCFO controller tracks the battery current exactly and settles faster in transitions than the PI controller does.

FIG. 5D is an exemplary graph illustrating power deviations observed for the HMS 100 with GSMCFO controller 120 under certain embodiments and observed with the PI controller in the Scenario 4 in comparison.

Solid line 522 represents an observed power deviation JP for the HMS with the GSMCFO Controller 120. Dashed-line 520 represents an observed power deviation ΔP for the HMS with the PI controller. Both lines 520 and 522 exhibited transition spikes with almost same peak values less than about 5% of switched power values. However, the GSMCFO controller settled within a second without overshooting, on the other hand, the PI controller struggled to maintain ΔP=0, due to overshooting and longer damping time.

Based on the performance comparison above described, it was confirmed that the GSMCFO controller 120 provides superior transient and steady-state performances to the PI controller. Moreover, the GSMCFO controller 120 demonstrated capability to keep the power balance (ΔP=0) of the HMS 100 despite the lumped disturbances in the system and sudden changes of the load demand and power generations.

The settling time and the overshoot comparison between the GSMCFO Controller 120 and the PI controller are presented in Tables 4 and 5, as below.

TABLE 4
Settling time (s) comparison: the PI
controller and the GSMCFO controller.
		ωr	Vdc	Ib	ΔP
	PI	3.6	7.3	3.6	4.5
	GSMCFO	0.7	0.2	0.8	0.8

TABLE 5
Overshoot (%) comparison: the PI
controller and the GSMCFO controller
		ωr	Vdc	Ib	ΔP
	PI	51.2.0%	32.7%	15.3%	12.8%
	GSMCFO	0.0%	0.0%	0.0%	0.0%

The above tables 4 and 5 further corroborate superior tracking performances of the GSMCFO to the PI controller in the presence of parametric uncertainties and external disturbances.

FIG. 6 is an exemplary illustration of flowchart for a method 600 of controlling a hybrid microgrid system (HMS), according to certain embodiments. The HMS includes a renewable energy source, for example, a wind turbine (WT) generator 102, 104 with a rotor side converter (RSC) 106 and a solar photovoltaic (PV) system 110 configured to output a PV output DC current to a DC-link in FIG. 1, a grid side converter (GSC) 108 configured to output a power to a point of common coupling (PCC) 128; a DC-link 132 configured to receive a power from the renewable energy source and to supply a power to the GSC; a rechargeable battery 114 configured to exchange a power via the DC-link; a load 126 configured to receive a power via the PCC 128; a utility grid 130 configured to exchange power via the PCC 128; and a controller 120 configured to control the HMS by executing a program and in accordance with the method defined as a global sliding mode control with fractional order terms (GSMCFO) method. The program comprises a definition set customized for the HMS and to be referred in applying the GSMCFO method to the HMS.

The method 600 includes steps illustrated by blocks in FIG. 6. One or more blocks may be combined or eliminated to achieve method for controlling the HMS 100, without departing from the scope of the present disclosure.

At step 610, a definition set to be referred in performing the GSMCFO method in controlling a HMS system, for example, the HMS 100 according to certain embodiments, is prepared and customized for the HMS 100. The definition set includes: a characteristic element c_i to be monitored, equations defining a desired value c_i* of the characteristic element c_i, a fractional order sliding mode (FOSM) surface ζ_i for the characteristic element c_i, and a control law element u_i^cnt of the characteristic element c_i. The equation defining the FOSM surface ζ_i(t) of the characteristic element c_i(t) include a fractional time integral of a tracking error e_i(t) and a fractional time derivative of the tracking error e_i(t). The tracking error e_i(t) for the characteristic element c_i(t) is given by,e_i(t)=c_i(t)−c_i*(t)  . (101)

At step 620, current status of the HMS is monitored and monitored information is gathered and stored in the controller. The current status to be monitored includes the characteristic element c_i(t) and a related status of the HMS appeared in the definition set.

At step 630, calculations of the equations defined in the definition set are performed by the controller 120, based on the characteristic element c_i(t) monitored and the related status of the HMS monitored.

At step 640, the controller 120 controls the HMS based on the control law element u_i^cnt(t) calculated and in accordance with the GSMCFO method.

Above steps or part of those steps are iterated until the FOSM surface ζ_i(t) or the tracking error e_i(t) reduces to values equal to or smaller than each criterion predetermined.

Based upon numerous examples and the experimental observations, an efficient and cost-effective hybrid microgrid system configuration was achieved by linking the PV system directly, that is without any converters, with the DC-link. This reduces the number of electronic converters and thus enhances efficiency and reduces cost of the HMS.

A controlled operation of the HMS was achieved using a GSMCFO controller 120. Four scenarios were provided to demonstrate the performance of the GSMCFO controller 120 under various power generation and load conditions. In each of these scenarios, the GSMCFO controller 120 demonstrated i) excellent transient and steady-state performances, ii) global stability of the HMS subjected to parametric uncertainties, variations of renewable power sources and load demand, iii) regulation of DC-link voltage irrespective of the operating conditions, iv) extracting maximum power from both the wind and PV systems by the RSC and GSC, respectively, and v) active power transfer to the grid, and maintaining the power balance of the hybrid microgrid by effective charging/discharging of the BESS.

Due to the robustness and fast convergence of the GSMCFO controller 120, the overshoots in ω_r, V_dc, I_b, and ΔP were 0.0%, and all settled at 0.7 s, 0.2 s, 0.8 s, and 0.8 s, respectively. The overshoot and settling time of the GSMCFO controller 120 was found to be highly satisfactory compared to the PI controller. Therefore, the GSMCFO controller 120 can improve the efficiency, the cost and the power balance of the hybrid microgrid.

Numerous modifications and variations of the present disclosure are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.

Claims

1. A hybrid microgrid system (HMS) for wind power electrical generation comprising: a wind turbine (WT) and an electric generator;a grid side converter (GSC) configured to output a power to a point of common coupling (PCC);a DC-link configured to receive a power from the WT and the electric generator and to supply a power to the GSC;a rechargeable battery configured to exchange a power between the DC-link;a load configured to receive a power via the PCC;a utility grid configured to exchange power via the PCC; anda controller comprising: a processor; a memory; a bus-line; and I/O port,

2. The hybrid microgrid system of claim 1, wherein the definition set further comprises: a minimum value SOCmin in and a maximum value SOCmax of the state of charge (SOC) of the rechargeable battery; and equations defining a power imbalance ΔP and a power balance condition of the HMS, given respectively as, ΔP=Pre+Pug−Pdem−Pb ΔP=0,wherein Pre represents a total power generated by the WT and the electric generator, Pug, a grid power exchanged between the utility grid and the PCC, Pdem, a load demand, Pb, a battery power exchanged between the rechargeable battery and the DC-link,wherein the controller is further configured tomonitor elements required to calculate a power imbalance ΔP and a SOC,calculate a power imbalance ΔP with the equation given in the definition set, andcontrol the battery power Pb and the grid power Pug to satisfy and maintain the power balance condition, under a restriction that the SOC of the rechargeable battery satisfies a condition, SOCmin≤SOC≤SOCmax.

3. The hybrid microgrid system of claim 2, wherein the WT comprises: a wind turbine generator and the electric generator comprises a rotor and a stator;a rotor side converter (RSC) configured to receive an AC power from the electric generator and output an RSC output DC current to the DC-link;a solar photovoltaic (PV) system configured to output a PV output DC current to the DC-link; anda bidirectional buck-boost converter (BBBC) connected between the DC-link and the rechargeable battery and configured to control a power exchanged between the rechargeable battery and the DC-link,and wherein, the definition set further comprises an equation defining an equivalent control law element uieqv for the characteristic element ci, wherein the equivalent control law element uieqv comprises a maximum disturbance term Rt1−μ δi, representing a possible maximum value of a lumped external disturbances and parametric perturbations to the tracking error ei(t), wherein, Rt1−μ represents a Riemann-Liouville fractional integration, and δi, a positive function,wherein, the equation defining the control law element uicnt(t) comprises a function SG (ζi(t)) given by a signum function sgn(ζi(t)) or one of its smooth approximations including

4. The hybrid microgrid system of claim 3, wherein the characteristic element c1 is an angular frequency ωr of the WT, and the characteristic element c2 is a d-axis stator current Ids, wherein the desired value ωr* for the angular frequency ωr of the WT is defined as,

5. The hybrid microgrid system of claim 3, wherein the DC-link further comprises a DC-bus and a DC-link capacitor, and wherein the characteristic element c3 is a DC-link voltage Vdc, the desired value Vdc* defined for the DC-link voltage Vdc is given as

6. The hybrid microgrid system of claim 3, wherein the GSC is further configured to output an AC output to a point of common coupling (PCC) via a grid side filter, wherein the characteristic elements ci(t) (i=4, 5) are a d-axis AC current Id (i=4) of the AC output from the GSC, and a q-axis AC current Iq (i=5) of the AC output from the GSC for, respectively,the desired values defined for the d-axis AC current and the q-axis AC current are given respectively as c4*(t)=Id*=Id=23⁢Pdem-PugVd,c5*(t)=Iq*=0,wherein Pdem and Pug represent a power demand at the load and a power exchanged between the PCC and the utility grid, respectively, wherein Pug>0, when provided from the utility grid to the PCC, Pug<0, when provided from the GSC to the utility grid, Vd, a d-axis AC voltage of the AC output from the GSC,the equations defining the FOSM surfaces ζi(t) for the d-axis AC current Id (i=4) and the q-axis AC current Iq (i=5) are given respectively as, ζ4=k4Rtμe4(t)+Rt1-μe4(t),ζ5(t)=k5Rtμe4(t)+Rt1-μe5(t),wherein μ ∈ (0, 1), k4, and k5 are positive constants, Rtμ and Rt1-μ each denotes a Riemann-Liouville fractional integration and a Riemann-Liouville fractional derivation, respectively,wherein the equations defining the equivalent control law element u4eqv(t) and the control law element u4cnt(t) of the d-axis AC current Id are given by d-axis AC voltages Vd eqv(t) and Vdcnt(t) of the AC output from the GSC, and defined respectively as,

7. The HMS of claim 3, wherein the BBBC is configured to facilitate charging of the rechargeable battery while operating as a buck converter, and to facilitate discharging to the DC-link while operating as a boost converter, and wherein the characteristic element c1(i=6) is a battery current Ib,the desired value Ib* defined for the battery current Ib is given as,