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A review of reactive power optimization algorithm in power system
- 1 Nanyang Institute Of Technology
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Abstract
The paper analyses the significance of reactive power optimization, generalizes the current situation of power system development. Various optimization algorithms were introduced in this paper such as traditional optimization algorithm, intelligence optimization algorithm, including the methods of linear programming, Newton’s method, heuristic optimization algorithm, etc. This research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous variables. The purpose of the research is to find the optimal solution of reactive power optimization algorithm, minimize the transport network loss of power system, and improve the quality of users.
Keywords
power system, reactive power optimization, traditional algorithms, intelligence optimization algorithm
[1]. Xiong Yuhui, Reactive Power Optimization of Power System [D]; Nan Chang University.2005
[2]. Chen Laiying, Lin Yu, Research on Intelligent algorithm for reactive Power Optimization of Power system,Adhesion,2020
[3]. Hoboson E,et al. Linear programming for power system real time control calculation[C],IFAS Conference Melbourne,1977
[4]. Albert M． Sasson, Combined use of the parallel and fletcher-powell, non-linear for optimal load flows [J] IEEE Transactions on Power ,Apparatus and Systems, 1969, 10: 1530-1537
[5]. Jabr R A,Coonick A H, Cory B J． A primal-dual interior Point Method for Power Flow Dispatching [J] IEEE Transactions on Power System, 2002,17( 3) : 654-662．
[6]. Liu MingBo, Chen Xuejun. Prime-dual affine scaling interior point method based reactive power optimization in power systems[J]. Power System Technology, 1998, 22( 3): 21-28
[7]. Liu MingBo, Chen Xuejun. Improved interior point method for reactive power optimization in power systems [J] Automation of Electric Power Systems, 1998, 22( 5): 33-36．
[8]. Andreas Rieder, Inexact Newton Regularization Using Conjugate Gradients as Inner Iteration, SIAM Journal on Numerical Analysis, 2005
[9]. Tang Huanwen, Qin Xuezhi. Practical Optimization Method[M]. Dalian: Dalian University of Technology Press, 1994
[10]. M. Dehghani, M. Mardaneh, O. P. Malik , Following Optimization Algorithm for Solving Power Engineering Optimization Problems, Journal of Operation and Automation in Power Engineering,pp 57-64,2020.
[11]. A. Lazar, ”Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets, ”Heuristics Optim. Knowl. Discovery,pp. 263-278, 2002.
[12]. Guo Chuangxin, Zhu Chengzhi, Zhao Bo, et al. Power system reactive power optimization based on an improved immune algorithms [J], Automation of Electric Power System ,2005,29(15):23-28
[13]. Peter J. M. van Laarhoven , Emile H. L. Aarts ,Simulated annealing, Simulated Annealing: Theory and Applications, pp 7–15,1987
[14]. Zhou Xiaojuan, Zhou Hui, The improved SA algorithm in VAR and voltage control of power system[I]. Relay,2004,32(1):24-27
[15]. Jia Dexiang, Tang Guoqing, Han Jing, Reactive power optimization of power system based on modified simulated annealing algorithm[J], Relay, 2004, 32(4):32-35
[16]. Eberhart R，Kennedy J.A new optimizer using particle swarm theory[C]// MHS'95.Proceedings of the Sixth,International Symposium on Micro Machine and Human,Science.IEEE, 2002
[17]. Shi Y.A Modified Particle Swarm Optimizer[C]// Proc.of IEEE ICEC conference，Anchorage.1998.
[18]. Liu Shukui, Reactive power optimization of power system based on adaptive focused particle swarm optimization[D],Cheng Du: Southwest Jiaotong University.2009
Cite this article
Ma,Y. (2023). A review of reactive power optimization algorithm in power system. Theoretical and Natural Science,5,886-891.
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Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
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