| 引用本文: | 吕忠,周强,蔡雨昌.含分布式电源的DEIWO算法配电网无功优化[J].电力系统保护与控制,2015,43(4):69-73. |
| Lü Zhong,ZHOU Qiang,CAI Yuchang.Reactive power optimization in distribution network with distributed generation on DEIWO algorithm[J].Power System Protection and Control,2015,43(4):69-73 |
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| 摘要: |
| 针对含分布式电源的配电网无功优化的特点，提出一种将入侵杂草算法与差分进化算法相结合的混合求解算法。该算法将一组初始可行解进行繁殖、空间扩散，当达到环境允许的最大值时，通过引入竞争机制，选取适应度较高的部分个体，再通过变异、交叉、选择，最终保留最佳个体。该算法既利用了入侵杂草算法结构简单、参数少和鲁棒性强的优点，又通过结合差分进化算法，克服其易陷入局部最优，精度不高的缺陷。以IEEE33节点系统进行仿真分析，并与传统的入侵杂草优化算法进行比较，结果表明该算法具有较强的全局搜索能力以及较高的收敛精度，能够有效 |
| 关键词: 配电网 分布式发电 无功优化 杂草入侵优化 差分进化 |
| DOI：10.7667/j.issn.1674-3415.2015.04.011 |
| 分类号: |
| 基金项目:国家自然科学基金高铁联合基金重点项目(U1134205)；中国铁路总公司科技研究开发计划课题(2014J009-B)；国家自然科学基金(51307143)；朔黄铁路发展有限责任公司科技开发项目(2012-607) |
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| Reactive power optimization in distribution network with distributed generation on DEIWO algorithm |
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Lü Zhong, ZHOU Qiang, CAI Yuchang
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School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
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| Abstract: |
| According to the feature of reactive power optimization in distribution network with distributed generation, a new kind of hybrid algorithm, which is composed of invasive weed optimization (IWO) and differential evolution (DE) is proposed. A group of initial feasible solutions are reproduced and spatial dispersed, some of individuals which are higher adaptation degree are selected by introducing competition mechanism when they reach the maximum allowable environment, and the best individual is retained through mutation, crossover and selection. The algorithm utilizes advantages of simple structure, less parameters and robustness of IWO, and overcomes the drawback of being trapped in local optimum and lower accuracy in combination with DE. Comparison with original IWO in IEEE 33-bus system simulation analysis, the results show that the algorithm has stronger global search capability and higher degree of convergence and can effectively reduce the power loss. |
| Key words: distribution network distribution generation reactive power optimization invasive weed optimization differential evolution |
