US-12620806-B2 - Method and computer system for generating a decision logic for a controller

Abstract

To generate a decision logic for a controller of an Industrial Automation Control System, IACS, an iterative process is performed in which a decision logic candidate for the decision logic is generated, and a performance of the decision logic candidate in response to scenarios is computed.

Inventors

• Adamantios MARINAKIS
• Yaman Cansin EVRENOSOGLU
• Ioannis Lymperopoulos
• Sandro SCHOENBORN
• Pawel DAWIDOWSKI
• Jan Poland

Assignees

• HITACHI ENERGY LTD

Dates

Publication Date

20260505

Application Date

20210818

Priority Date

20200819

Claims (20)

1. 1 . A computer-implemented method of providing a decision logic for a controller of a protection device in an Industrial Automation Control System (IACS) of a power system, the method comprising: an iterative process including plural iterations that respectively include automatically generating a decision logic candidate for the decision logic, and computing a performance of the decision logic candidate in response to scenarios, wherein computing the performance comprises performing system simulations; selecting at least one decision logic from the decision logic candidates based on a result of the computed performances; and deploying the selected at least one decision logic to the protection device, to initiate execution of the selected at least one decision logic, during live operation of the IACS, wherein the selected at least one decision logic, when executed, provides a protection function to the power system, and wherein the protection function comprises determining whether or not to trip a circuit breaker within the power system.
2. 2 . The method of claim 1 , wherein a scenario-creating logic is executed to create at least part of the scenarios used in the iterative process.
3. 3 . The method of claim 2 , wherein the scenario-creating logic is an adversarial logic to a decision logic generator that automatically generates the decision logic candidates in the iterative process, and/or wherein the scenario-creating logic is a machine learning model or comprises a machine learning model.
4. 4 . The method of claim 2 , wherein the scenario-creating logic learns iteratively while parameters of at least one of the decision logic candidates are updated and/or decision logic candidates having different machine learning model architectures are challenged by the scenarios in the system simulations, and/or wherein the scenario-creating logic learns with an objective of causing at least one of the decision logic candidates to underperform in accordance with a performance-metric or several performance metrics.
5. 5 . The method of claim 2 , wherein the scenario-creating logic is constrained to only generate scenarios that are within a system specification of the IACS.
6. 6 . The method of claim 1 , further comprising: storing scenarios that cause a decision logic candidate having a first machine learning model architecture to underperform; retrieving at least some of the stored scenarios; and determining a performance of at least one further decision logic candidate having a second machine learning model architecture different from the first machine learning model architecture in response to the retrieved scenarios.
7. 7 . The method of claim 1 , wherein computing the performance comprises combining computed values of at least one performance metric determined for a decision logic candidate for a batch of scenarios, optionally wherein combining the computed values comprises weighting the computed values based on a frequency of occurrence of the scenarios in the batch during field operation of the IACS.
8. 8 . The method of claim 1 , wherein generating the decision logic candidates comprises training machine learning models, optionally wherein at least part of the scenarios used in the iterative process is created by a scenario-creating logic and the scenario-creating logic and the decision logic generator are a generative adversarial network (GAN).
9. 9 . The method of claim 8 , wherein the decision logic candidates generated in the iterative process comprise machine learning models having two or more different machine learning model architectures, optionally wherein the two or more different machine learning model architectures comprise artificial neural network architectures differing from each other in a number of nodes and/or a number of layers.
10. 10 . The method of claim 8 , wherein generating decision logic candidates in the iterative process comprises: selecting a machine learning model architecture; training a decision logic candidate having the machine learning model architecture until a first termination criterion is fulfilled; storing the performance computed for the trained decision logic candidate having the machine learning model architecture; if a second termination criterion is not fulfilled, repeating the training and storing steps for a different decision logic candidate having a different machine learning model architecture; if a second termination criterion is fulfilled, selecting one of the decision logic candidates based on the stored performances.
11. 11 . The method of claim 1 , wherein a complexity of machine learning models used as decision logic candidates is increased as new decision logic candidates are being generated in the process of generating the decision logic.
12. 12 . The method of claim 1 , further comprising adjusting a learning rate for a decision logic generator that generates the decision logic candidate while the decision logic generator learns with the aim to generate decision logic candidates that perform better in the system simulations.
13. 13 . The method of claim 1 , wherein the performance is computed in accordance with a performance metric or several performance metrics.
14. 14 . The method of claim 13 , further comprising receiving, via an interface, an input specifying the performance metric or the several performance metrics, and/or dynamically changing the performance metric or several performance metrics during the method of generating the decision logic.
15. 15 . The method of claim 13 , wherein the performance metric or several performance metrics include one or several of: minimizing cost of electricity, increasing grid power transfer limits, ensuring grid stability, maximizing protection objectives of security and dependability, keeping voltages and currents within limits, or maximizing economic benefit.
16. 16 . The method of claim 1 , wherein performing the system simulations comprises simulating a behavior of primary and/or secondary devices of the power system, optionally wherein performing the system simulations comprises one or several of: power flow simulations, short-circuit calculations, electromagnetic transient calculations, optimal power flow computation, or unit commitment analysis.
17. 17 . The method of claim 1 , wherein the system simulations comprise one or more of currents, voltages, phasors, synchrophasors in lines, cables, or bus bars of the power system.
18. 18 . The method of claim 1 , wherein the method is performed by one or several integrated circuits that execute: a decision logic generator that generates and outputs the decision logic candidate; a scenario-providing module that outputs a batch of active scenarios, wherein the scenario-providing module comprises a scenario-creating logic; a simulation engine coupled to the decision logic generator and the scenario-providing module and operative to perform the system simulation for scenarios included in the batch of active scenarios using the decision logic candidate; a performance assessor that computes the performance of the decision logic candidate for the scenarios included in the batch of active scenarios; and a coordinator that coordinates operation of the decision logic generator and of the scenario-providing module responsive to an output of the performance assessor, optionally wherein the coordinator is operative to control adversarial machine learning models of the decision logic generator and the scenario-providing module, further optionally wherein the coordinator controls a learning rate of the adversarial machine learning models of the decision logic generator and the scenario-providing module.
19. 19 . The method of claim 1 , further comprising: executing, by the controller of the protection device in the IACS, the selected at least one decision logic; optionally further comprising automatically modifying the selected decision logic in response to a monitored field behavior of the selected at least one decision logic executed by the controller.
20. 20 . A computing system for generating a decision logic for a controller of a protection device in an Industrial Automation Control System (IACS) of a power system, comprising one or several integrated circuits operative to: perform an iterative process having iterations that respectively include automatically generating a decision logic candidate for the decision logic, and computing a performance of the decision logic candidate in response to scenarios, comprising performing system simulations; select at least one decision logic from the decision logic candidates based on a result of the computed performance; and deploy the selected at least one decision logic to the protection device, to initiate execution of the selected at least one decision logic, during live operation of the IACS, wherein the selected at least one decision logic, when executed, provides a protection function to the power system, and wherein the protection function comprises determining whether or not to trip a circuit breaker within the power system.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application is a national stage entry of International Patent Application No. PCT/EP2021/072979, filed on Aug. 18, 2021, which claims priority to European Patent Application No. 20191649.1, filed on Aug. 19, 2020, which are both hereby incorporated herein by reference as if set forth in full. FIELD OF THE INVENTION The present disclosure relates to methods and devices for generating a decision logic of a controller of an industrial automation control system (IACS), in particular a power transmission, power distribution or generation system. More specifically, the present disclosure relates to computer-implemented techniques for generating such a decision logic. BACKGROUND OF THE INVENTION Modern industrial automation control systems (IACS), such as power generation systems, power distribution systems, power transmission systems, power grids, or substations, and modern industrial systems include a vast number of components. The decision logic of protection devices such as protection relays of such systems decides which one of various actions is to be taken under which circumstances. For illustration, in real-time operation of an electric power utility, transmission and distribution system equipment, including transformers, overhead lines, underground cables, series/shunt elements etc. are protected by means of a measurement system (voltages, currents), digital relay, and a circuit breaker. The control logic which is deployed in a digital relay utilizes the measured signals, identifies whether there is a severe fault which should be cleared to avoid damage to system equipment and finally sends a signal to the circuit breaker to open. Fast identification and clearance of faults are essential for the reliability and the security of the overall system. The decision logic (i.e. protection logic) for each relay as well as the coordination scheme between multiple relays are designed and tested under anticipated grid scenarios. Conventionally, this is done by human expert engineers. During the design phase, engineers perform simulations of faults and other disturbances such as switching events in a grid to evaluate and refine the performance of the protection control-logic. The performance metrics are usually set by the prevailing practices for a given grid. Once deployed, the protection logic remains unchanged, until errors in its performance are observed. Due to proliferation of converter-interfaced generation which introduces more stochasticity to the electricity supply temporally and spatially replacing the conventional generators, and e-mobility which introduces more stochasticity to demand, designing of protection systems are increasingly challenging as the grid operates closer to its limit. In addition, due to the lack of short circuit current capacities and different nature of short circuit currents (e.g. delayed zero crossings, distorted signals etc.) provided by converter-interfaced generators as well as the multi-directional currents due to changing spatial patterns of generation, it is desirable for the protection systems to adjust to changing environments. As a result, the development of protection logic becomes an increasingly complex task. Furthermore, this task will need to be performed increasingly often as the suitability of the protection system will require to be reevaluated more frequently due to changes in grid infrastructure as well as generation/demand patterns. A conventional practice for protection logic design is such that, for each specific design case, an expert engineer selects among a plurality of protection functions (such as overcurrent, directional, distance, differential protection) or a combination of them and determines the settings (i.e. function parameters) associated with the selected function(s). The objective is to come up with a set of protection functions and logic between them such that the security (i.e. the success rate of activating the protection system when necessary) and dependability (i.e. the success rate of not activating the protection system if not necessary) of the protection system are maximized while being able to respond to any fault as fast as possible. The expert engineer achieves this by anticipating potential events (e.g. faults and non-faults, such as inrush conditions, switching operations etc.) which are relevant to the protection design task at hand and performs numeric simulation (e.g. simulation of electromagnetic transients, post-fault steady-state short circuit analysis) of each of the selected events in order to identify the resulting signals which will be observed by the protection relays, based on which the protection function(s) will operate on the field. This allows one to define and test the protection functions, logic and settings for all anticipated events in order to ensure the correct decision. Typically, this is performed by following a set of engineering practices fine-tuned by the