基于反向云自适应粒子群算法的多目标无功优化

doi:10.11930/j.issn.1004-9649.201705051

中国电力 ›› 2018, Vol. 51 ›› Issue (7): 21-27.DOI: 10.11930/j.issn.1004-9649.201705051

基于反向云自适应粒子群算法的多目标无功优化

曹生让^1,2, 丁晓群¹, 王庆燕³, 张静³

1. 河海大学能源与电气学院, 江苏南京 210098;
2. 江苏联合职业技术学院南京分院, 江苏南京 210019;
3. 金陵科技学院机电工程学院, 江苏南京 211169

收稿日期:2017-05-30 修回日期:2018-03-04 出版日期:2018-07-05 发布日期:2018-07-31
作者简介:曹生让(1982-),男,博士研究生,副教授,从事分布式发电和无功优化研究,E-mail:cao820728@126.com;丁晓群(1956-),男,教授,博士生导师,从事人工智能在电力系统中的应用、电气设备故障诊断及配电自动化等研究,E-mail:289054636@qq.com
基金资助:
江苏省青年科学基金资助项目（BK20150115）。

Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization

CAO Shengrang^1,2, DING Xiaoqun¹, WANG Qingyan³, ZHANG Jing³

1. College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;
2. Jiangsu Union Technical Institute, Nanjing Branch, Nanjing 210019, China;
3. Institute of Technology and Electrical Engineering, Jinling Institute of Technology Electrical Engineering, Nanjing 211169, China

Received:2017-05-30 Revised:2018-03-04 Online:2018-07-05 Published:2018-07-31
Supported by:
This work is supported by Jiangsu Youth Science Fund Project (No. BK20150115).

摘要/Abstract

摘要： 针对粒子群算法在高维复杂问题寻优时易陷入局部寻优的现象,提出了反向云自适应粒子群算法(OCAPSO)，通过反向学习加快算法的收敛速度，使用云模型来平衡粒子的全部搜索和局部搜索能力，使用自适应突变机制增强种群的多样性。用高维广义Schwarz函数对OCAPSO的有效性进行验证，进一步以IEEE30节点系统进行单目标和多目标无功优化测试并将测试结果与粒子群优化（PSO），进化算法（EA）等测试结果进行比较，证实了该算法的优越性。分析表明，OCAPSO算法用于解决多目标无功优化问题有效可行。

关键词: 无功优化, 粒子群优化, 反向学习, 云模型, 自适应, 多目标

Abstract: An opposition-based cloud model adaptive particle swarm optimization algorithm (OCAPSO) is presented to solve the high-dimensional problems that the conventional PSO algorithm is easy to fall into a locally optimized point. The algorithm convergence speed is accelerated through opposition-based learning, and the cloud model is used to balance the global and local search ability of each particle, and the adaptive mutation mechanism is used to enhance the population diversity. The effectiveness of OCAPSO is verified by high-dimensional generalized Schwarz function. Then single objective and multi-objective reactive power optimization of IEEE30 bus system are tested. The superiority of OCAPSO is confirmed by comparing with the testing results of PSO and EA. Analysis shows that OCAPSO is effective for multi-objective reactive power optimization.

Key words: reactive power optimization, particle swarm optimization, opposition-based learning, cloud model, adaptive, multi-objective

中图分类号:

曹生让, 丁晓群, 王庆燕, 张静. 基于反向云自适应粒子群算法的多目标无功优化[J]. 中国电力, 2018, 51(7): 21-27.

CAO Shengrang, DING Xiaoqun, WANG Qingyan, ZHANG Jing. Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization[J]. Electric Power, 2018, 51(7): 21-27.

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https://www.electricpower.com.cn/CN/Y2018/V51/I7/21

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基于反向云自适应粒子群算法的多目标无功优化

Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization

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基于反向云自适应粒子群算法的多目标无功优化

Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization

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