| 引用本文: | 王希,王昕,李立学,郑益慧,徐清山.基于动态云进化粒子群算法的风电系统无功优化方法[J].电力系统保护与控制,2013,41(24):36-43.[点击复制] |
| WANG Xi,WANG Xin,LI Li-xue,ZHENG Yi-hui,XU Qing-shan.Reactive power optimization for wind power system based on dynamic cloud evolutionary particle swarm optimization[J].Power System Protection and Control,2013,41(24):36-43[点击复制] |
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| 摘要: |
| 针对风电系统中，风力的不确定性导致粒子的适应度不稳定性较大、劣性粒子偏多，难以快速收敛到最优值，进而造成系统电压偏差较大，网损剧增的问题，提出了基于动态云进化粒子群算法对风电系统进行无功优化。首先以网损最小作为优化目标建立了风电系统无功优化模型。然后提出动态云进化粒子群算法。该算法根据粒子的适应度值，选取优秀个体进行进化，从而降低劣性粒子比例，增强搜索速度。再通过云发生器，使得优秀个体进化出的优秀种群趋于正态分布，从而达到改善粒子分布的目的。在此基础上，根据正态云的分布特点，动态改变飞行速度，进一步改善粒子分布、提高搜索精度。最后以风电系统的有功网损为优化目标，进行补偿容量的确定，仿真结果证明了该方法的有效性。 |
| 关键词: 风电系统 无功优化 动态云 云进化 粒子群 |
| DOI：10.7667/j.issn.1674-3415.2013.24.006 |
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| 基金项目:国家自然科学基金(60504010) ；国家高新技术863发展计划(2008AA04Z129)，流程工业综合自动化国家重点实验室开放课题基金资助 |
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| Reactive power optimization for wind power system based on dynamic cloud evolutionary particle swarm optimization |
| WANG Xi1,WANG Xin1,LI Li-xue1,ZHENG Yi-hui1,XU Qing-shan2 |
| () |
| Abstract: |
| For wind power system, the uncertainty of wind leads to the instability of the particle' fitness and more pessimum particles, so it's difficult to quickly converge to the optimal value, which causes the large system voltage deviation and the sharp increase of network loss. A Dynamic Cloud Evolution Particle Swarm Optimization (DCEPSO) algorithm is proposed to realize the reactive power optimization of wind power system. Firstly, the minimum network loss is designed to be the optimization goal of reactive power optimization model of wind power system. Secondly, the DCEPSO algorithm is presented. According to the particle's fitness value, the algorithm selects excellent individuals to evolve, which reduces the proportion of pessimum particle and increases the search speed. Then through the cloud generator, excellent population evolved by excellent individuals tends to normal distribution, so as to improve the particle distribution. On this basis, according to the characteristics of the normal cloud distribution, dynamically changing speed can further improve the particle distribution and the search precision. Finally, the active network loss of wind power system is made to be the optimization goal to determine the capacity of compensation. The simulation results prove the effectiveness of the proposed method. |
| Key words: distribution system reactive power optimization dynamic cloud cloud evolution particle swarm optimization |
