Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control
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 quantitat...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
China electric power research institute
2024-01-01
|
| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10375965/ |
| _version_ | 1797216175681175552 |
|---|---|
| author | Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si |
| author_facet | Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si |
| author_sort | Tianyun Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T11:41:47Z |
| format | Article |
| id | doaj.art-87ca8af92a2e43699f01cb14288dcc3a |
| institution | Directory Open Access Journal |
| issn | 2096-0042 |
| language | English |
| last_indexed | 2024-04-24T11:41:47Z |
| publishDate | 2024-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj.art-87ca8af92a2e43699f01cb14288dcc3a2024-04-09T19:47:11ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422024-01-01101132810.17775/CSEEJPES.2023.0019010375965Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective ControlTianyun Zhang0Jun Zhang1https://orcid.org/0000-0001-6908-2671Feiyue Wang2Peidong Xu3Tianlu Gao4Haoran Zhang5Ruiqi Si6School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072Institute of Automation, Chinese Academy of Sciences,State Key Laboratory for Management and Control of Complex System,Beijing,China,100190School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072School of Electrical Engineering and Automation, Wuhan University,Wuhan,Hubei,China,430072In 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.https://ieeexplore.ieee.org/document/10375965/AI quantitative intelligence assessment and self-evolutionautomated reinforcement learningBayesian optimizationcorrective controlparallel system |
| spellingShingle | Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control CSEE Journal of Power and Energy Systems AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system |
| title | Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control |
| title_full | Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control |
| title_fullStr | Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control |
| title_full_unstemmed | Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control |
| title_short | Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control |
| title_sort | parallel system based quantitative assessment and self evolution for artificial intelligence of active power corrective control |
| topic | AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system |
| url | https://ieeexplore.ieee.org/document/10375965/ |
| work_keys_str_mv | AT tianyunzhang parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT junzhang parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT feiyuewang parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT peidongxu parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT tianlugao parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT haoranzhang parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol AT ruiqisi parallelsystembasedquantitativeassessmentandselfevolutionforartificialintelligenceofactivepowercorrectivecontrol |