Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

Tianyun Zhang; Jun Zhang; Feiyue Wang; Peidong Xu; Tianlu Gao; Haoran Zhang; Ruiqi Si

doi:10.17775/CSEEJPES.2023.00190

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Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

Special Section Papers | 更新时间：2026-10-15

- Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control
- Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第1期页码：13-28
- 作者机构：
  
  1. School of Electrical Engineering and Automation, Wuhan University,Wuhan,China
  2. State Key Laboratory for Management and Control of Complex System, Institute of Automation, Chinese Academy of Sciences,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.00190
  中图分类号：
- 纸质出版：2024-01-01
- 稿件说明：
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Tianyun Zhang, Jun Zhang, Feiyue Wang, 等. Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):13-28.

Tianyun Zhang, Jun Zhang, Feiyue Wang, et al. Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 13-28.
Tianyun Zhang, Jun Zhang, Feiyue Wang, 等. Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):13-28. DOI： 10.17775/CSEEJPES.2023.00190.

Tianyun Zhang, Jun Zhang, Feiyue Wang, et al. Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 13-28. DOI： 10.17775/CSEEJPES.2023.00190.

摘要

Abstract

In artificial intelligence (AI) based-complex power system management and control technology

one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However

there is

currently

nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article

based on a parallel system framework

a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems

by resorting to human intelligence evaluation theories. On this basis

this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed

taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent

and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results

effectively

as well as intuitively improving its intelligence level through self-evolution.

关键词

Keywords

references

1. F. Chollet. ( 2019 Nov. ). On the measure of intelligence [Online ] . Available: https://arxiv.org/abs/1911.01547.

7. A. Kelly, A. O’ Sullivan, P. de Mars, and A. Marot. ( 2020, Mar. ). Reinforcement learning for electricity network operation [On line ] . Available: https://arxiv.org/abs/2003.07339.

15. T. Schaul, J. Togelius, J. Schmidhuber. ( 2011, Sep. ). Measuring intelligence through games [Online ] . Available: https://arxiv.org/abs/1109.1314.

24. Y. T. Chen, A. Huang, Z. Y. Wang, I. Antonoglou, J. Schrittwieser, D. Silver, and N. de Freitas. ( 2018, Dec. ). Bayesian optimization in AlphaGo [Online ] . Available: https://arxiv.org/abs/1812.06855. .

30. P. I. Frazier. ( 2018, Jul. ). A tutorial on Bayesian optimization [Online ] . Available: https://arxiv.org/abs/1807.02811.

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