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

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外文摘要：In artificial intelligence(AI)based-complex power system management and control technology,one of the ur-gent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution.However,there is,cur-rently,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.

外文关键词：

AI quantitative intelligence assessment and self-evolutionautomated reinforcement learningBayesian optimizationcorrective controlparallel system

作者：

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

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作者单位：

School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,Hubei,China

State Key Laboratory for Management and Control of Complex System,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

基金：

National Key R&D Program of ChinaScience and Technology Project of SGCC(State Grid Corporation of China)

项目编号：

2018AAA0101504

出版年：

2024

DOI：

10.17775/CSEEJPES.2023.00190

中国电机工程学会电力与能源系统学报(英文版)

中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI

ISSN：2096-0042

年,卷(期)：2024.10(1)

参考文献量36