Patent Application
20240022110
This disclosure relates to power electronics and more specifically to a hierarchical architecture that generates and provides power to the power grid and provides cyber intrusion detections.
The power grid is an interconnected network that delivers electricity to users. It includes power stations that generate power, electrical substations that step up voltages, electrical power transmission lines that transport power, and electric power distribution stations that step down voltages.
In the contiguous United States, there are three power grids. They are the Eastern power grid, the Western power grid, and the Texas power grid. The vastness of these power grids make them vulnerable to cyclical demand and cyber-attacks.
Decarbonizing the power gird and generating energy from renewable sources also poses challenges to the power grid. The current integration of discrete renewable power sources and energy storage feeding the power grid does not provide a consistent and reliable source or distribution to where it is needed.
The system may be better understood with reference to the following drawings and description. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like-referenced numerals designate corresponding parts throughout the different views.
An autonomous reconfigurable process and/or system (referred to as a system(s)) increases the reliability of the power grid and reduces power outages. The system increases the capacity of the power grid, provides renewable resources to respond to unexpected demands, and allows for maintenance by allowing other power sources to be taken off-line for testing, monitoring, and repairs without disrupting service.
The systems' hierarchical controller monitors aggregate power flow through the power grid in real time, ensuring a constant and consistent supply. The subsidiary controllers that comprise the systems' hierarchical controller monitor each branch of the system in real time while mitigating cyber intrusions and threats using artificial intelligence.
The autonomous reconfigurable systems of
In
Vc represents the magnitude of the instantaneous capacitor voltage. The modular converter 208 (also referred to as the modular front-end) is ‘ON’ when Vterminal-node=Vc; it is ‘OFF’ when Vterminal-node=0. The front-end half-bridges produce a wide range of medium and/or high grid utility voltage that meet the Institute of Electrical and Electronics Engineers (IEEE) 519 standards, which do not require a high switching frequency. The IEEE 519 standards define the voltage and current harmonics distortion criteria for the design of electrical systems.
In
A second multi-state converter shown as the solar panel module 212 that is part of each of the photovoltaic submodules 202 cascades a modular inverter to the first H-bridge that sources an isolation transformer. The isolation transformer requires little maintenance because of its durable construction. The secondary of the isolation transformer is connected to a second H-bridge forming a dual active bridge that sources the photovoltaic power and provides the electrical isolation between sources and loads. In
In alternative autonomous reconfigurable systems, the solar panel modules 212 and/or energy storage modules 210 are modified by adding and/or substituting a direct current to alternating current converter, e.g., instead of a dc-dc converter, to integrate other power sources such as wind turbines, for example, to the power grid. Similarly, electric vehicle chargers are alternatively connected through a similar dc-dc converter that are part of the solar panel modules 212 in some autonomous reconfigurable systems.
In
Benefits of the renewable source integration include mitigating the low inertia phenomena common to solar power plants. During alternating grid events that lead to large frequency and voltage variations, power from the embedded energy storages, solar panels, and/or dc links also emulate a synchronous generator which actively dampens frequency and voltage variations, and decreases the likelihood of brownouts and/or shutdowns. The systems also mitigate alternating current transmission bottlenecks by providing effective damping control and additional dynamic voltage support to the alternating current power grid, which also improves the transfer capability of congested alternating current transmission lines. The systems support operational stability through disturbance control that dampens the detected oscillations that occur in asynchronous power grids.
Some autonomous reconfigurable systems also provide direct power sources to electric power distribution stations and/or load centers. These systems link direct current sources directly into large urban areas to overcome issues associated with distributed generation. The systems integrate renewable sources of energy into weak and vulnerable regional power grids and/or the national grid. The autonomous reconfigurable systems also harmonize the operation of multiple electrical sources. Unlike the patchwork of discrete power generation, the disclosed autonomous reconfigurable systems execute hierarchical control strategies that include measurements from renewable energy sources such as solar power panels and/or energy storage systems, for example, processed by an integrated control system 302 (e.g., in the form of a hierarchical control system) that minimizes and/or dampens harmonic noise and eliminates the need for competing control systems. Further, the systems' scalability provides the capability to integrate other energy resources including other/different renewable energy sources in a common physical location (e.g., within one plant or within one physical location) and provides connectivity to different alternating current and direct current system platforms.
In a use case, the central processing unit (CPU) controller 304 receives power dispatch commands from one or more system operators and/or other sources that include commands related to power transferred to the alternating current side (Pac-ref), power transferred to direct current side (Pdc-ref), and reactive power provided to the alternating current side (Qac-ref). In response, the integrated control system 302 provides voltage and frequency support to the alternating current grid, maintains the power dispatched from the integrated system, controls direct current link voltages and alternating current/direct currents, and provides energy balancing control 1206 between the submodules 202, 204, and 206. The CPU controller 304 responds to a synchronous generator emulation function 1204 and determines arm modulation indices (marm) and also determines the reference power commands transmitted to the photovoltaic submodules 202 and the energy storage system submodules 204 (Ppv-ref and Pess-ref). The reference power commands for the photovoltaic submodules 202 and the energy storage system submodules 204 may depend upon the power dispatch commands, additional power requirements from the power generator (based on the synchronous generation emulation function) of the autonomous reconfigurable system (ΔP and ΔQ), the maximum available photovoltaic power (Ppv-mppt 1208), and/or the energy storage system submodules' rating (Pess-rating). The CPU controller 304 sends the modulation indices of each arm, the referenced photovoltaic power, and the referenced energy storage systems power to a field programmable gate array controller 306.
The CPU controller 304 controls the arms output of the autonomous reconfigurable system as an aggregate to maintain a grid-source and a circuit stability, which means that the CPU controller 304 does not directly control each individual submodule 202-206. The FPGA controller 306 disaggregates the monitored variables and issues commands that maintain the stability in each individual module and submodule through a capacitor voltage balancing 1214.
The field programmable gate array (FPGA) controller 306 sums the capacitor voltages from each arm (ΣVcap) and sums the absolute value of the difference between individual capacitor voltages from the average of the capacitor voltages of the modules in each arm (Σ|ΔVcap|). In
The digital signal processors shown as the DSP controllers 308 in
In photovoltaic submodules 202, the DSP controller 308 identifies the maximum power that can be generated by the photovoltaic sources (Ppv-mppt) and controls the voltage at the terminals of the photovoltaic submodules 202. The DSP controllers 308 also control the inductor current based on the photovoltaic power reference (Ppv-ref) transmitted from FPGA controller 306. In the energy storage system submodules 204, each DSP controller 308 determines the state-of-charge (SOC) 1210 of its voltage storage, and controls the power required from the voltage storages and the inductor current based on the energy storage systems power reference (Pess-ref) received from FPGA controller 306. The DSP controller 308 also generates the switching commands for the dc-dc converter switches in the photovoltaic submodules 204 and energy storage system submodules 202 based on the duty cycle ratios that are generated.
In some controllers, artificial intelligence (AI) makes bad command and corrupt/bad data detections in response to legacy data, infected operating states and/or internal control states of the system. Based on comparisons, a neural network trained by a training engine 1224, detects, isolates, and/or mediates malicious commands and/or bad data received externally or received from the systems including through its sensors. Some alternative trained neural networks comprise feedforward artificial neural networks that generate neuron nodes with each novel training dataset sample.
In a use case, bad data may be detected by reading data. In
When the error count is not greater than a second predetermined threshold at 512 in
To predict arm currents and/or capacitor voltages in an autonomous reconfigurable system, a recurrent dynamic network such as a model based on autoregressive network with exogenous inputs is used in some systems. In a use case, arm currents and capacitor voltages are predicted using a nonlinear autoregressive network with an exogenous inputs model. The nonlinear autoregressive network with exogenous inputs model comprises a recurrent neural network that uses a current timestep as well as previous timestep inputs and previous timestep outputs to determine next time-step output. The model uses a combination of a hidden layer and output layer to identify the output. Each layer incorporates multiple neurons that use the sum of incoming weighted normalized data and passes it through an activation function to generate the output data from the neuron. An exemplary model with two layers is shown in
A process flow for constructing an artificial intelligence model and its exemplary evaluation by a training engine 1224 is shown via
The requesting training engine 1224 then constructs one or more neural network models at 706 that detect cyber intrusions and trains one, two or more models 708 (referred to as a model) such as one two or more neural networks using learning data representations or a training dataset stored in memory 1242 and/or a remotely accessible memory based on the operating conditions and use cases.
In some alternative use cases, the training data represents the operating conditions that directly precede the effects of a cyber intrusion such as the operating state conditions that directly precede the effects of the injections or executions of unauthorized commands, receipt or processing modified data or bad data, etc. By detecting the operating systems' conditions that precede the effects of a cyber intrusion and/or the effects processing bad data, the system may shut down or isolate some or all of its at risk components before the cyber threat commands or bad data cause the system to become unstable, its code or portions of its hardware to become unstable, and/or cause the system operate in an unintended and/or unauthorized manner. In some alternative systems, the detection of cyber threats and/or bad data may automatically initiate customized operating processes recited in potential crash profiles stored in memory 1242 that mitigate the cyber threat and/or bad data threat before its affects occur making the system resistant to the undesired effects of the cyber intrusions and/or bad data. The operating policies may be enforced based on the monitored device's behavior of the systems, or based on one or more particular users' (e.g., a device and/or person) behavior. In a use cases, the behavior precedes device failures and the effect of the cyber intrusion or the processing of bad data.
Training may occur through a fixed number of iterations, a predetermined amount of time, and/or repeatedly until the constructed model hits and/or reaches a fitness threshold during a training session at 708. Some engines 708 train by iteratively reading a training dataset set a predetermined number of times while iterative tuning and/or modifying the model's configuration (e.g., the models' topology such as changing a circuit or functional blocks interconnections with other functional blocks or circuits). At 710, the trained model/models are evaluated by the training engine 1224 by processing an evaluation dataset that is separate from and different from the training dataset. Based on the trained models' performance, the training evaluation engine calculates a fitness value or an average fitness value at 712 or a plurality of fitness values when multiple models are evaluated. In some use cases, a user or an application defines the evaluation or fitness function that training evaluation engine executes. When the threshold is exceeded the model(s) at 714 are rendered as trained model(s). When it/they do not exceed the fitness value, the training session repeats 702.
When a nonlinear autoregressive network with exogenous inputs model 1226 is used to correlate variables, such as arm currents and capacitor voltages across the modules (ΣVcap, Σ|ΔVcap|) to alternating current-side voltages and internal control signals (modulation indices), for example, large sized models are rendered in some use cases. As the number of inputs and outputs increase, an exponential rise in the number of neurons and synapses are generated. To avoid the exponential rise in the size of some neural networks, such as the networks implemented with nonlinear autoregressive networks with exogenous inputs models 1226, the input-output combinations are split in some use cases and optimized to generate smaller sized multiple of neural networks including those executing nonlinear autoregressive network with exogenous inputs models. That is, instead of correlating variables directly, such as alternating current-side voltages and internal control signals to arm currents and voltages using a single neural network executing a nonlinear autoregressive network with exogenous inputs model in this exemplary use case, multiple neural networks executing nonlinear autoregressive networks with exogenous inputs models (e.g., ten or more) are developed to correlate the inputs to the outputs that predict the states in the autonomous reconfigurable system. For example, each phase modulation index (mj, j∈a, b, c) and the corresponding alternating current-side voltage are processed to generate the predicted alternating current-side current of the phase. Similarly, each arm's modulation index (mx, j, j∈a, b, c, x∈p, n) and the corresponding arm current are processed to generate the predicted voltages (ΣVcap, Σ|ΔVcap|) of the corresponding arm. And, each phase's circulating modulation indices are processed to generate the predicted circulating currents.
In a use case, estimates of the arm currents and/or voltages from a neural network executing a nonlinear autoregressive network with exogenous inputs models are compared to the measured values by the process of
In another use case, the autonomous reconfigurable system was evaluated in simulations and hardware-in-the-loop tests. The simulations and hardware-in-the-loop test setup shown in
The simulator 1226 developed models, predict arm currents and voltages that are evaluated under different operating conditions. The operating conditions are based on changes to the commanded alternating current-side power, direct current-side power, photovoltaic power, and energy storage systems power. The operating conditions differ from the operating conditions used to train the models. Additionally, the prediction of the arm currents and voltages by the models were evaluated under abnormal operating conditions that would be caused by failure events in the power grid like alternating current-side transmission line faults and the loss of power generators. The phase arm current, summation of submodule capacitor voltages (ΣVcap), and summation of absolute value of the difference between the submodule capacitor voltage (Σ|ΔVcap|) from models (used for predictions) and from measured electromagnetic transient model simulations are plotted in
Under simulated (1) channel faults, (2) command modifications, (3) system losses, and (4) normal operations the autonomous reconfigurable system was monitored. Under each simulated condition, the detection algorithms and models were evaluated to measure their effectiveness in detecting bad commands and bad data. The tests showed that the cyber security intrusion detection system and its detection algorithm were effective in detecting bad commands and data.
The cloud/cloud services or memory 1242 and/or storage disclosed also retain an ordered listing of executable instructions for implementing the processes, system functions, and features described above in a non-transitory machine or computer readable code. The machine-readable medium may selectively be, but not limited to, an electronic, a magnetic, an optical, an electromagnetic, an infrared, or a semiconductor medium. A non-exhaustive list of examples of a machine-readable medium includes: a portable magnetic or optical disk, a volatile memory, such as a Random-Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM) or a Flash memory, or a database management system. The cloud/cloud services and/or memory 1242 may comprise a single device or multiple devices that may be disposed on one or more dedicated memory devices or disposed within one or more of the controllers 304, 306, and/or 308, customized circuit or other similar device. When functions, steps, etc. are “responsive to” or occur “in response to” another function or step, etc., the functions or steps necessarily occur as a result of another function or step, etc. A device or process that is responsive to another requires more than an action (i.e., the process and/or device's response to) merely follow another action.
The term “engine” refers to a processor or a portion of a program that determines how the programmed device manages and manipulates data. For example, a training engine 1224 includes the tools for forming and training artificial intelligence and/or neural networks. The term “substantially” or “about” encompasses a range that is largely in some instances, but not necessarily wholly, that which is specified. It encompasses all but a significant amount, such as what is specified or within five to ten percent. In other words, the terms “substantially” or “about” means equal to or at or within five to ten percent of the expressed value. Forms of the term “cascade” and the term itself refer to an arrangement of two or more components including circuits such that the output of one circuit is the direct input of the next circuit (e.g., in a series connection). The term “real-time” and “real time” refer to responding to an event as a detection occurs, such as making corrections or changing power supply/source configurations in response to measurements as they are made or commands as they are received. A real time operation are those operations which match external activities and proceed at the same rate (e.g., without delay) or faster than that rate of the external activities and/or an external process. Some real-time autonomous reconfigurable systems operate at a faster rate as the physical element it is controlling. The term communication, in communication with, and versions of the term are intended to broadly encompass both direct and indirect communication connections.
The autonomous reconfigurable systems disclosed herein may be practiced in the absence of any disclosed or expressed element (including the hardware, the software, and/or the functionality expressed), and in the absence of some or all of the described functions association with a process step or component or structure that are expressly described. The systems may operate in the absence of one or more of these components, process steps, elements and/or any subset of the expressed functions.
Further, the various elements and autonomous reconfigurable system components, and process steps described in each of the many systems and processes described herein is regarded as divisible with regard to the individual elements described, rather than inseparable as a whole. In other words, alternate autonomous reconfigurable systems encompass any variation and combinations of elements, components, and process steps described herein and may be made, used, or executed without the various elements described (e.g., they may operate in the absence of) including some and all of those disclosed in the prior art but not expressed in the disclosure herein. Thus, some systems do not include those disclosed in the prior art including those not described herein and thus are described as not being part of those systems and/or components and thus rendering alternative systems that may be claimed as systems and/or methods excluding those elements and/or steps.
The autonomous reconfigurable system improves the responsiveness of power producers and power distributors. The systems increase the reliability of the power grid and reduce power deficits because they increase the capacity of the power grid beyond its conventional sources. The systems also serve as a power reserve for unexpected demands. The systems' hierarchical controller monitors aggregate power flow through the power grid in real time, allowing the systems and its operators to ensure a constant and consistent power grid-flow that meets consumer's changing demand. The controllers that comprise the system's hierarchical controller monitor each branch of the autonomous reconfigurable system that sources the power grid and automatically shift and/or provide electricity supply to high demand areas when it is needed in real time. Moreover, the disclosed technology mitigates the threat of cyber intrusions by monitoring commands including the commands its receives from the system operators and the data processed by the hierarchical controller through artificial intelligence.
Other systems, methods, features and advantages will be, or will become, apparent to one with skill in the art upon examination of the figures and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the disclosure, and be protected by the following claims.
This application claims priority to U.S. Provisional Patent Application No. 63/389,887, titled “Large-Scale Power Electronics—Circuit Architecture and Hierarchical Architecture for Control and Cyber Intrusion Detection”, which was filed on Jul. 16, 2022 that is herein incorporated by reference.
These inventions were made with United States government support under Contract No. DE-AC05-00OR22725 awarded by the United States Department of Energy. The United States government has certain rights in the inventions.
| Number | Date | Country | |
|---|---|---|---|
| 63389887 | Jul 2022 | US |
