It is difficult to apportion the power contribution of each of multiple DC power sources while maintaining a desired DC bus voltage. The present invention generally relates to a centralized controller for DC boost converters.
The disclosed invention is a distributed control system for operating a DC bus fed by disparate DC power sources that service a known load or unknown load. The individual voltage sources vary in v-i characteristics and have maximum supply capacities that are time-varying. Each source is connected to the bus via a boost converter. The boost converters may have different dynamic characteristics and power transfer capacities, but are all controlled through PWM.
The primary problem addressed herein is to track the time-varying power sources and apportion the power contribution of each while maintaining the DC bus voltage within the specifications. A central digital controller is developed that solves the steady-state system for the optimal duty cycle settings that achieve a desired power supply apportionment scheme for a known or a predictable DC load. A distributed networked control system is then derived from the central system that utilizes communications among controllers to compute a shared estimate of the unknown time-varying load through shared bus current measurements and common (replicated) bus voltage measurements.
Various embodiments will be described in detail with references to drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but these are intended to cover application or embodiments without departing from the spirit or scope of the claims attached hereto. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting.
1. DC Bus Configuration
A DC bus with a single variable load fed by multiple sources is shown in
1.1 Control Philosophy and PowerFlow
The general objective of any of the many possible control schemes is to maintain the bus voltage within some specified range in response to load and input voltage variations. The multiple distributed sources have time-varying power capacities, so the control scheme must also continually adjust the converter input currents to respect the changing power limits while maintaining the bus voltage. The power flow equations are:
Let Psourcemax be the instantaneous maximum power available from all sources, and Psimax be the instantaneous maximum power available from the ith source so:
Psourcemax≧Psource≧PLoad+PLoss (6)
eij1i≦Psimax (7)
According to (6) the total loss plus load must never exceed the maximum available source power. If (6) is not violated, then the control problem is to properly apportion the power outputs of the boost converters according to the local constraints defined by (7) and the admissible bus voltage range. If (6) is violated, load trimming control must be implemented. We will consider only the case where (6) is an equality for now and assume that the schedules Psimax are known with certainty. Under these circumstances, the control problem involves following the source power schedule while regulating vb in response to changing input voltages and load schedule. If perfect knowledge of both source and load schedules obtains, then predicted control regimes can be calculated and control modes switched in and out accordingly. The remainder of this paper will discuss a control scheme that relies of source/load schedules and DC models of the aggregate bus dynamics.
1.2 Steady State Analysis
The average state-space system for the boost converters operating in continuous conduction mode are given by
where xi=j1i, xN+1=vb, and ui=ei. Equations of the form given in (8) describe the local converter's KVL, and (9) gives the bus KCL. For the DC steady state, the LHS of (8) and (9) at zero. At steady-state the equations are:
solving (11) and substituting into 10:
Let Ro be
so collecting terms gives:
Let vi=Roλij1i=Roj2i, and let
then substitute into (14):
When vi′=0, the other sources provide no power and ei>0 ensures j1i>0. When other sources are active on the bus, the input current provided by the source at voltage ei is reduced by contributions from other sources. Since the boost converters are unidirectional, j1i≧0. For a positive power contribution from the source, j1i>0, so:
ei>λivi′ (17)
ei>λi(vb−vi) (18)
Alternatively, the upper limit on λi is decreased:
So converters operating collectively will provide less current and at higher duty cycles for given ei and vb than if operating alone. If all boost stages are exactly identical and driven by the same or identical voltage sources, the “homogenous element” case, (11) becomes:
and (10) is:
e−rji−Nλ2Roj1=0 (21)
Solving for j1 and λ:
Generally r<<Ro, so the input impedance seen by each voltage source increases by a factor of N. Each converter provides 1/N of the current that would be delivered by a single converter system.
Since jb=NRoj2, λ is the same as that of a single converter. Alternatively, (21) can be substituted on j to express it in terms of vb:
Quadratic formula solution to (25) yields:
When N=1, the solution is as expected:
and if r=0, or r<<Ro, the familiar form appears:
Recall that the maximum power available from a voltage generator with generation resistance r is:
Then (28) can be expressed as
and (27) as
Where Po is the total bus power. The term NPmax in (32) is the total power available from N stages. Alternatively, let 1/N=α and substitute into (24):
So (35) and (36) present the solution for λ in terms of the fraction α of the total power provided to the load by a single stage. Note also that the input current for each stage can be determined by
(37) indicates that the effective load seen by a single converter is αGo, i.e., the total load is apportioned among the collective equally. Note that in the limit α→0 or N→∞,
j1→0. Moreover, the total losses through all converters is Nrj12, so since the input current j1 in a collective configuration is about j1/N of the single converter configuration, the losses scale as 1/N. This encourages a modular building-block approach based on a replicated low-power unit that can be composed in parallel to mediate higher power sources. The α factor can be generalized to the heterogenous collective.
1.3 Output Current Apportionment
Describe the ith output current as:
α is a convex set that apportions the current supplied by each converter.
Substitute (39) into (10):
Solve for λi by the quadratic equation:
which is an indexed version of (35). The term αiGo is the effective (reduced) load admittance allocated to the ith converter. Let
So α is a partition of the total bus load assigned to each converter. The set α can also be viewed as apportioning the power output Pio of each converter:
Provided the set α represents an admissible apportionment that respects the maximum power capacity of each individual converter for a given (known) load Go, the duty cycle can updated by each converter controller according to (42) in a decentralized scheme.
Now givb is the ith converter output current j2i. Set vb to the desired reference voltage, vb=vb* and Govb*=jb*, the bus reference current, and substitute into (42):
Equation (14) is a decentralized, feedforward algorithm in terms of the ith sensed input voltage ei, the bus reference voltage vb*, the bus reference current jb*, and the αi the ith apportionment factor. It relies on knowledge of the effective input resistance ri, (generator resistance+line resistance+inductor resistance+switch resistance), and Go, the total bus load. In most cases of interest, the actual value of Go is unknown and time-varying. Consequently, jb* is also unknown. In the case of ideal homogenous collective,
Since each converter takes an independent measurement of vb, vbi, knowledge of N−1, the number of other converters on the bus, and the local output current measurement, j2i, provide a means to estimate Go. In practice, the measurements vbi, and j2i are noisy so an estimator is needed to predict Go. Moreover, the converters certainly not likely to be exact replicas, so modeling errors will be uncompensated without feedback. The same holds true for an inhomegenous collective by definition. In this case the bus current jb is unknown at each converter. But the collective has distributed knowledge of the value and through a timely sharing protocol, the value of jb may be determined through the sum
and knowledge that all converters sample and report the values synchronously. Against sampling clocks are generally out of phase and communications channels have latencies, usually uncertain, so to estimate jbi an estimator that accounts for the sampling jitter and communications delay (in addition to sensor noise) must be found. Moreover, all converters must arrive at the same estimate of jb close enough in time to coherently adjust their outputs while maintaining vb within specification and ensuring ji respects its specification based on equation (7). Since we also assume that load controller may have a known load schedule, it may transmit a new value for Go over the network to the converter controllers, but again the arrival at each converter is subject to uncertain communication delays and intraarrival jitter. Finally, for heterogenous collectives, the controllers must have a protocol for arriving at the set α.
Since the available power from a source may change according to a schedule, the collective must complete a new apportionment agreement based on some policy at each change. For now we assume the schedule consists of values for α with event times, i.e., a vector time sequence
A={α(t1),α(t2), . . . α(tM)} (51)
Each controller has a local copy of A and all controllers are synchronized to a common clock. At each time step in A the controllers pick their respective values for αi(tk) and adjust the duty cycle according to (47).
2. Development of Control Concepts
In this section we develop control concepts based on the ideal steady-state derivations of the previous section. First, a centralized control system for ideal collectives is developed. The idealizations are based on two assumptions: 1) certain and correct knowledge of model parameters; and 2) perfect synchronization of events through a global clock.
Once the performance envelope has been established for the ideal central controller, a distributed control system model is developed. The developments are guided by the use of model-based adaptive control concepts in which control agents perform computations and communications in real time. The computations involve combining sensor readings with models and optimization routines. Model structure, parameters, and optimization cost functions are shared through communications protocols at appropriate times.
Local sensor data is also shared periodically among control agents. In general, control designs are evaluated according to the frequency and amount of information sharing required, and a performance metric based on the specifications for bus voltage and converter currents. For our purposes, let the state vector be
x=[j11,j12, . . . j1N,vb] (52)
and the desired state be x*, which are the reference values for the input currents and bus voltage. The control strategy is to track the changing reference currents and bus voltage. Usually, the bus voltage reference is constant and has a tolerance of ±Δvb, but variations in bus voltage are possible with model-based controls.
For now assume the bus voltage is regulated and the input currents are tracked. The goal is to respond in real time to disturbances in the load, Go, and the input voltage vector e, and to changes in power apportionment policy summarized in the convex set α.
Apportionment policy must always respect the changing limits to source power capacity, but may impose other factors within those bounds based on reliability, ramp-up time, economic and environmental factors, and other considerations. This requires reasoning about the specifics of the source generators, and involves defining a preferred mix of generation through cost functions and a subsequent optimization that determines α over a given epoch.
2.1 General State-Space Model
A general state space model for the system and controller is given in
The state equations for a bus with N converters are
HG{dot over (x)}=(FG−RG)x+PGU (53)
AG=HG−1(FG−RG) (54)
BG=HG−1PG (55)
{dot over (x)}=AGx+BGu,y=CGx (56)
where HG and RG are diagonal matrices of size N+1, FG is a skew symmetric matrix of size N+1, PG is a N+1 diagonal, and the input voltage vector u (ei) is an N×1 vector. The last row and column of the system is occupied by the DC bus equation.
The elements of the system matrices are
where Li is the ith inductor, and CT=ΣCi+Cb+ΣCg, i.e., sum of converter output capacitances, native bus capacitance, Cb, and the sum of load capacitances, ΣCg. The dissipation matrix represents the losses from the converters and the total load admittance on the bus, where
Go=ΣGi+Gb, the total of all individual conductances that are directly connected to the bus, including any native bus conductance. The state feedback matrix is a skew-symmetric
matrix with element values of λi. The input gain matrix PG, is an identity matrix if all source converters are boost converters. Otherwise it has 1 s for boost converter entries and duty cycle settings for buck converters. The control vector u is
composed of the source voltages and an auxiliary current source or load, j3, applied directly to the bus. If j3 is negative, it represents a current sink, otherwise it's a
current source, enabling multi-bus interconnections. Recall the state vector is given by (52) and consists of the source currents and the bus voltage. The output vector is the vector
of individual converter output currents, the bus voltage, and the total bus current. The controller
ΓG measures x, y, and u, and outputs FG and PG: it essentially adapts AG and BG and as such is an adaptive state feedback and adaptive gain controller. However, because they represent average duty cycles for PWM-based control, physical limits on the values of FG constrain entries to 0≦fij≦1. The values of fij constrain the eigenvalues of the system.
The controller selects the AG matrix (and BG if buck converters are used to mediate higher voltage sources) in equation (56), to move the state x(tk) to the desired reference state x*(tk+1) as determined by the model calculations, in response to the disturbances in input voltage u and load. Load disturbances manifest in the Go parameter of the RG matrix, so the system is linear time-varying (LTV), but is LTI between load disturbances and control interventions.
The steady state equations corresponding to equation (53) are
(FG−RG)x+PGu=0 (57)
u=PG−1(RG−FG)x (58)
x=(RG−FG)1PGu (59)
so for a known load (RG is fixed), known duty cycles (FG is fixed), and for x=x*, the input vector ([ej3]) can be computed from (58). Alternatively, the state x can be computed from known load, duty cycles and input voltages from equation (59).
2.2 General Controller Architecture
The Model-Based Reference Generator computes steady-state values for the input and output current references j1i*, j2i*, and/or the duty cycle reference λi*, for use in direct or closed loop control algorithms. The module takes as inputs the bus voltage reference vb*, input voltage measurements, loss resistances ri, and the load conductance, Ĝo. The Model-Based Reference Generator can implement any decentralized or distributed optimization calculation, or equations (27), (44) or (47).
Reference value for the state variables are provided to feedforward/feedback control loops that measure the converters' states and output the duty cycle commands. Values for the duty cycles can also be fed directly to the PWM actuators in an open-loop feedforward control scheme. Gains for discrete control algorithms are calculated by the Control Algorithm Gain Calculation module. K* is a vector of gain values for generalized PID control loops. Values of converter inductance, input resistance, capacitance (
2.3 Steady-State Feedforward Controller
The estimate of the local output current reference j2i* in (58) can be determined from the local measurement of j2i(k) by
Control agents can also share current measurements on each control cycle through a network protocol to obtain jb(k)=Σj2i(k) and obtain ĵ2i(k)=αijb(k). This approach requires high-tempo communications but can compensate for different converter dynamics by ensuring a common basis for current apportionment at each control cycle.
Each control agent estimates its apportioned load conductance (or current) based on the agreed-upon set α, resulting in power contributions from each converter/source in proportion to α at steady state. The apportionment set α must be updated according to power sharing protocols conducted by all generation control agents when source power capacity changes require a new generation power flow operating point, but operation is decentralize during intervals of constant α.
The Source Power Sharing Policy determines α from an interactive agreement protocol based on the optimal power capacity envelope P*, which is a time-varying quantity that captures natural variations in generation power in sources such as wind and solar, preferences for reducing fuel consumption and/or CO2 emissions from fossil-fueled sources, etc.
2.4 Adaptive Cascade PI Controller
Note that if the DC boost converters have different dynamics, individual compensators are needed to adjust the transient performance of the system in response to changes in load and input voltage. In some cases, estimation errors and the differing dynamics may prevent settling to the proper steady state apportionments. The “α” strategy apportions the common lead among converters. However, power sources are limited by their respective power envelopes and may have variable losses, requiring apportionment of the power input of each boost converter.
An optimization and scheduling protocol is conducted among source and load control agents to obtain a maximum power schedule, P={P1max(t), P2max(t), . . . PNmax(t)} for each source based on efficiency, reliability, fuel usage, CO2 emissions, availability of variable sources (wind and solar), and other considerations. From the Pimax value for the current epoch, the maximum input current j1imax is found by dividing by the input voltage measurement for the current control cycle.
The j1imax values are shared over the network whenever one or more changes, and a proportion
is computed. This provides a proportional gain in analogy to αi for bus load apportionment, but applied to the input current.
The βi parameter computed by the Input Power Apportionment PI Gain Stage—
Controllers share gains KP1 and KI1, so the ith controller in the Laplace domain obeys
Hence the input current reference signals remain in proportion throughout time in response to the common bus voltage error. The gains KP
The current reference j1i* is also fed into a feed-forward Model Stage (
in one form of the adaptive gain strategy for converging duty cycles in unison.
There are many variations on the theme for the controller architecture of
This application claims the benefit of U.S. Provisional Application No. 61/794,484 filed Mar. 15, 2013, titled DISTRIBUTED CONTROL SYSTEM FOR PARALLEL-CONNECTED DC BOOST CONVERTERS.
This invention was made with government support under grant number DE-AC04-94AL85000 awarded by the United States Department of Energy. The government has certain rights in the invention.
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Number | Date | Country | |
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20140365147 A1 | Dec 2014 | US |
Number | Date | Country | |
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61794484 | Mar 2013 | US |