| 基于自适应变异特性粒子群算法的无功优化 |
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| 引用本文： | 靳现林,马安,安成万. 基于自适应变异特性粒子群算法的无功优化[J]. 山西电力, 2008, 0(2): 7-10 |
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| 作者姓名： | 靳现林 马安 安成万 |
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| 作者单位： | 1. 国华能源投资有限公司,北京,100007
2. 晋城市供电分公司,山西,晋城 048000 |
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| 摘 要： | 针对电力系统无功优化问题,提出了1种自适应变异特性粒子群算法来克服粒子群优化方法容易早熟而陷入局部最优解的缺点。该方法以种群适应度方差为量化指标,动态衡量和监视粒子群体的聚集情况,并对聚集的粒子赋予变异操作,用以提高整个群体的全局寻优能力。通过对IEEE-6和IEEE-30测试系统的无功优化问题计算及结果分析表明该方法快速、高效、准确。
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| 关 键 词： | 粒子群优化 无功优化 变异 方差 |
| 文章编号： | 1671-0320(2008)02-0007-03 |
| 修稿时间： | 2007-11-23 |
Reactive Power Optimization Based on Adaptive Mutation Particle Swarm Optimization Algorithm |
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| JIN Xian-lin,MA An,AN Cheng-wan. Reactive Power Optimization Based on Adaptive Mutation Particle Swarm Optimization Algorithm[J]. Shanxi Electric Power, 2008, 0(2): 7-10 |
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| Authors: | JIN Xian-lin MA An AN Cheng-wan |
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| Affiliation: | JIN Xian-lin , MA An , AN Cheng-wan (1. Guohua Energy Investment Co. , Ltd, Beijing 100007, China; 2. Jincheng Power Supply Company, Jincheng, Shanxi 048000, China; 3. Shanxi Electric Power Company, Taiyuan, Shanxi 030001, China) |
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| Abstract: | To deal with reactive power optimization problem,an adaptive mutation particle swarm optimization algorithm(AMPSO)is presented to avoid the premature phenomenon in PSO.In AMPSO,swarm fitness variance is applied to judge and monitor particles' aggregation condition and mutation operator is acting on aggregating particles to improve method's global searching ability.The proposed algorithm is tested on IEEE-6 and IEEE-30 bus systems.The results show its better performance on celerity,accuracy and efficiency. |
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| Keywords: | particle swarm optimization(PSO) reactive power optimization mutation variance |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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