Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems

doi:10.1109/ACCESS.2020.3028064

	Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems
	Yin, Linfei 3; Luo, Shikui 3; Wang, Yaoxiong 1; Gao, Fang 3; Yu, Jun 2
刊名	IEEE ACCESS
	2020
卷号	8
关键词	Voltage control Optimization Power system stability Learning (artificial intelligence) Static VAr compensators Heuristic algorithms Coordinated secondary voltage control artificial emotional reinforcement learning complex-valued encoding dragonfly algorithm automatic voltage regulation multi-generator power systems
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.3028064
通讯作者	Gao, Fang(fgao@gxu.edu.cn)
英文摘要	This article proposes a coordinated optimization and control algorithm for coordinated secondary voltage control (CSVC) in multi-generator power systems. Firstly, to obtain a smaller voltage deviation and avoid the curse of dimensionality simultaneously, an artificial emotional reinforcement learning (AERL) is applied to automatic voltage regulation (AVR). Secondly, to obtain a smaller fitness value with lesser random for the decentralized independent variables optimization problem of the CSVC, a complex-valued encoding dragonfly algorithm (CDA) is proposed. Thirdly, the CDA and the AERL are coordinated for the CSVC and the AVR in multi-generator power systems. To verify the control performance of the AERL and the convergence of the proposed CDA, three simulation cases, i.e., IEEE 57-bus, 118-bus and 300-bus systems, are considered. The simulation results show that the CDA-AERL effectively obtains the smallest control objectives and the convergence for the CSVC in multi-generator power systems.
资助项目	Guangxi Natural Science Foundation[AD19245001] ; Guangxi Natural Science Foundation[2020GXNSFBA159025] ; National Natural Science Foundation of China[U1736123] ; National Natural Science Foundation of China[61773359] ; National Natural Science Foundation of China[61720106009] ; University of Science and Technology of China (USTC) Research Funds of the Double First-Class Initiative[YD2350002001]
WOS关键词	OPTIMIZATION ALGORITHM ; WIND ; SCALE ; STABILITY ; TRACKING ; MATRIX ; FLOW
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000578635500001
资助机构	Guangxi Natural Science Foundation ; National Natural Science Foundation of China ; University of Science and Technology of China (USTC) Research Funds of the Double First-Class Initiative
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/104542]
专题	中国科学院合肥物质科学研究院
通讯作者	Gao, Fang
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China 3.Guangxi Univ, Coll Elect Engn, Nanning 530004, Peoples R China
推荐引用方式 GB/T 7714	Yin, Linfei,Luo, Shikui,Wang, Yaoxiong,et al. Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems[J]. IEEE ACCESS,2020,8.
APA	Yin, Linfei,Luo, Shikui,Wang, Yaoxiong,Gao, Fang,&Yu, Jun.(2020).Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems.IEEE ACCESS,8.
MLA	Yin, Linfei,et al."Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems".IEEE ACCESS 8(2020).