| 引用本文: | 赵 乔,王增平,董文娜,鲍 薇.基于免疫二进制粒子群优化算法的配电网故障定位方法研究[J].电力系统保护与控制,2020,48(20):83-89.[点击复制] |
| ZHAO Qiao,WANG Zengping,DONG Wenna,BAO Wei.Research on fault location in a distribution network based on an immune binary particle swarm algorithm[J].Power System Protection and Control,2020,48(20):83-89[点击复制] |
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| 摘要: |
| 针对二进制粒子群算法在复杂规模化配电网故障定位时收敛速度慢、易陷入局部最优解的缺陷，提出一种基于免疫二进制粒子群优化算法的配电网故障定位方法。首先，应用免疫系统信息处理机制对粒子群算法进行改进，在算法进化过程中建立记忆细胞单元存储优质抗体，避免抗体种群更新后的群体退化。其次，引入抗体浓度调节机制与免疫选择操作保持抗体种群多样性，强化算法全局搜索能力，防止算法早熟。在此基础上，构建亲和度评价函数将改进后的算法应用于配电网故障定位。仿真结果表明，基于免疫二进制粒子群优化算法的配电网故障定位方法能够有效提升算法收敛速度与故障定位准确率，且在故障信息畸变情况下具有良好的容错性能。 |
| 关键词: 配电网 故障定位 粒子群算法 免疫机制 |
| DOI：DOI: 10.19783/j.cnki.pspc.191527 |
| 投稿时间：2019-12-11修订日期：2020-03-13 |
| 基金项目:国家电网公司总部科技项目资助(521710180008) “面向自愈的智能配电网免疫机制与模型研究” |
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| Research on fault location in a distribution network based on an immune binary particle swarm algorithm |
| ZHAO Qiao,WANG Zengping,DONG Wenna,BAO Wei |
| (1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric
Power University, Beijing 102206, China; 2. Zhengzhou Power Supply Company, State Grid Henan
Electric Power Company, Zhengzhou 450000, China) |
| Abstract: |
| The binary particle swarm optimization algorithm has shortcomings, such as slow convergence and the fact that it is easy for it to fall into the local optimal solution. Here a fault location method based on immune binary particle swarm optimization algorithm is proposed. First, the information processing mechanism of the immune system is applied to improve the particle swarm algorithm, and memory cell units are built to store high-quality antibodies during population evolution to avoid population degradation after antibody population updating. Secondly, the mechanism of antibody concentration regulation and immune selection are introduced to maintain the diversity of the antibody population, strengthen the global search ability of the algorithm and prevent premature algorithm. Finally, the improved algorithm is applied to the fault location of a distribution network by constructing an affinity evaluation function. The simulation results show that the algorithm based on immune binary particle swarm optimization can effectively improve the convergence speed and fault location accuracy, and has good fault tolerance in the case of fault information distortion.
This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. 521710180008) “Immune Mechanism and Model Research for Self-cure Oriented Intelligent Distribution Network”. |
| Key words: distribution network fault location particle swarm optimization immune mechanism |
