祝洪博;徐刚刚;海冉冉;余立平;

• 1:东北电力大学电气工程学院
• 2:东北电力大学自动化工程学院
• 3:洛阳市供电公司

摘要(Abstract)：

粒子群算法存在着早熟的现象,易陷入局部最小点,为了克服这个缺点,文章首先将云模型引入粒子群算法,将粒子分成2部分,靠近最优粒子和远离最优粒子的部分,其中靠近最优粒子种群的惯性权重由云模型的X-条件发生器自适应调整,提出了云自适应粒子群算法(cloud adaptiveparticle swarm optimization,CAPSO),然后引入梯度的思想,提出云自适应梯度粒子群算法(cloud adaptive gradientparticle swarm optimization,CAGPSO)。以网损最小为目标函数,对标准IEEE 14和IEEE 30节点系统进行仿真计算,结果表明改进后的CAGPSO算法能够获得更好的优化解。

关键词(KeyWords)： 云理论;网损最小;云自适应梯度粒子群算法;无功优化

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 祝洪博;徐刚刚;海冉冉;余立平;

Email:

DOI: 10.13335/j.1000-3673.pst.2012.03.031

参考文献(References)：

• [1]刘科研,盛万兴,李运华.基于改进遗传模拟退火算法的无功优化[J].电网技术,2007,31(3):13-18.Liu Keyan,Sheng Wanxing,Li Yunhua.Research on reactive poweroptimization based on improved genetic simulated annealingalgorithm[J].Power System Technology,2007,31(3):13-18(inChinese).
• [2]刘科研,盛万兴,李运华.基于改进免疫遗传算法的无功优化[J].电网技术,2007,31(13):11-16.Liu Keyan,Sheng Wanxing,Li Yunhua.Reactive power optimizationbased on improved immunity genetic algorithm[J].Power SystemTechnology,2007,31(13):11-16(in Chinese).
• [3]陈奇,郭瑞鹏.基于改进遗传算法与原对偶内点法的无功优化混合算法[J].电网技术,2008,32(24):50-54.Chen Qi,Guo Ruipeng.A hybrid reactive power optimizationalgorithm based on improved genetic algorithm and primal-dualinterior point algorithm[J].Power System Technology,2008,32(24):50-54(in Chinese).
• [4]袁晓辉,王乘,张勇传,等.粒子群优化算法在电力系统中的应用[J].电网技术,2004,28(19):14-19.Yuan Xiaohui,Wang Cheng,Zhang Yongchuan,et al.A survey onapplication of particle swarm optimization to electric power systems[J].Power System Technology,2004,28(19):14-19(in Chinese).
• [5]聂宏展,张冰冰,王新,等.基于改进粒子群优化算法的电力市场下的无功优化[J].电网技术,2007,31(21):85-90.Nie Hongzhan,Zhang Bingbing,Wang Xin,et al.Research onMPSO algorithm based reactive power optimization in electricitymarket[J].Power System Technology,2007,31(21):85-90(inChinese).
• [6]Shi Y,Eberhart R C.A modified particle swarm optimizer[C]//Proceedings of the IEEE Conference on EvolutionaryComputation.Piscataway,NJ:IEEE Press,1998:69-73.
• [7]刘建华,樊晓平,瞿志华.一种惯性权重动态调整的新型粒子群算法[J].计算机工程与应用,2007,43(7):68-70.Liu Jianhua,Fan Xiaoping,Qu Zhihua.New particle swarmoptimization algorithm with dynamic change of inertia weights[J].Computer Engineering and Applications,2007,43(7):68-70(inChinese).
• [8]路石俊,李翔,林艳婷.基于云模型的变电站运营成本分析[J].电网技术,2010,34(6):205-209.Lu Shijun,Li Xiang,Lin Yanting.Analysis on substation operatingcosts based on cloud model[J].Power System Technology,2010,34(6):205-209(in Chinese).
• [9]岳训,孙忠林,张艳琦,等.基于云模型的Web日志数据挖掘技术[J].计算机应用研究,2001(11):113-116.Yue xun,Sun Zhonglin,Zhang Yanqi,et al.Data mining technologyin web logs based on the cloud model[J].Application Research ofComputers,2001(11):113-116(in Chinese).
• [10]戴朝华.搜寻者优化算法及其应用研究[D].成都:西南交通大学,2009:28-31.
• [11]李军军,吴燕翔,甘世红,等.基于梯度PSO算法的PID参数整定[J].科学技术与工程,2009,9(9):2463-2467.Li Junjun,Wu Yanxiang,Gan Shihong,et al.PID parameters tuningbased on gradient PSO algorithm[J].Science Technology andEngineering,2009,9(9):2463-2467(in Chinese).
• [12]李德毅,刘常昱,杜鹢,等.不确定性人工智能[J].软件学报,2004,15(11):1583-1594.Li Deyi,Liu Changyu,Du Yi,et al.Artificial Intelligence withUncertaintyb[J].Journal of Software,2004,15(11):1583-1594(inChinese).
• [13]李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995(6):15-20.Li Deyi,Meng Haijun,Shi Xuemei.Membership clouds andmembership cloud generators[J].Computer R&D,1995(6):15-20(inChinese).
• [14]Xuexia Zhang,Weirong Chen,Wenzhao Cai.Chaohua Dai.Dynamicmulti-groups self-adaptive differential evolution algorithm with localsearch for reactive power optimization[C]//Proceedings of the 2009Asia-Pacific Power and Energy Engineering Conference,Wuhan,China:PES,Wuhan University,Wuhan Science and TechnologyBureau,Chinese Society for Electrical Engineering and EngineeringScience,2009:2911-2914.
• [15]Zhu Yunfang,Dai Chaohua,Chen Weirong,et al.Adaptiveprobabilities of crossover and mutation in genetic algorithm based oncloud generators[J].Journal of Computational Information Systems,2005,1(4):671-678.