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

文献类型：期刊论文

作者Zhang, Tianyun1; Zhang, Jun1; Wang, Feiyue2; Xu, Peidong1; Gao, Tianlu1; Zhang, Haoran1; Si, Ruiqi1
刊名CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
出版日期2024
卷号10期号:1页码:13-28
关键词AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system
ISSN号2096-0042
DOI10.17775/CSEEJPES.2023.00190
通讯作者Zhang, Jun(jun.zhang.ee@whu.edu.cn)
英文摘要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.
资助项目National Key R&D Program of China[2018AAA0101504] ; Science and Technology Project of SGCC (State Grid Corporation of China)
WOS研究方向Energy & Fuels ; Engineering
语种英语
WOS记录号WOS:001166437000035
出版者CHINA ELECTRIC POWER RESEARCH INST
资助机构National Key R&D Program of China ; Science and Technology Project of SGCC (State Grid Corporation of China)
源URL[http://ir.ia.ac.cn/handle/173211/57915]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Zhang, Jun
作者单位1.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Hubei, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Tianyun,Zhang, Jun,Wang, Feiyue,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.
APA Zhang, Tianyun.,Zhang, Jun.,Wang, Feiyue.,Xu, Peidong.,Gao, Tianlu.,...&Si, Ruiqi.(2024).Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control.CSEE JOURNAL OF POWER AND ENERGY SYSTEMS,10(1),13-28.
MLA Zhang, Tianyun,et al."Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control".CSEE JOURNAL OF POWER AND ENERGY SYSTEMS 10.1(2024):13-28.

入库方式： OAI收割

来源：自动化研究所

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