基于AIBPSO算法的含DG主动配电网分区故障定位  

Partition fault location of active distribution network with DG based on AIBPSO algorithm

作　　者：张小凤 李昂[1] 周雷金 王文妍 ZHANG Xiaofeng;LI Ang;ZHOU Leijin;WANG Wenyan(School of Electrical Engineering,Shaanxi University of Technology,Hanzhong 723000,China)

机构地区：[1]陕西理工大学电气工程学院,陕西汉中723000

出　　处：《供用电》2024年第12期93-101,共9页Distribution & Utilization

基　　金：陕西省教育厅专项科研计划(15JK1125)。

摘　　要：为解决有源配电网故障定位时定位速度慢、准确率低的问题,提出了一种将人工免疫二进制粒子群(artificial immune binary particle swarm optimization,AIBPSO)算法和穷举搜索法相融合的分区优化定位策略。首先,利用Tent混沌映射产生的混沌序列初始化种群,避免种群初始化过程中出现“过早收敛”的问题。其次,将人工免疫算法与二进制粒子群算法相结合,对种群个体进行交叉变异操作,引入抗体浓度、免疫操作环节维持抗体种群多样性,利用免疫记忆环节存储优质抗体,避免抗体种群在更新过程中出现种群退化,不断刷新种群。再次,构建含分布式电源的主动配电网故障定位的分区优化模型,通过二次降维的方式减少故障区段的搜索维度。最后,通过仿真测试验证了基于AIBPSO算法和穷举搜索法的配电网分区故障定位策略,在处理多分支复杂配电网的单重和多重故障时具有良好的可行性,尤其是在信息畸变的多分支配电网场景下可展现出高准确性、快速性的显著优势。To address the issue of slow positioning speed and low accuracy in fault location within large-scale active distribution networks,a partition optimization location strategy that integrates the artificial immune binary particle swarm optimization(AIBPSO)algorithm with the exhaustive search method has been proposed.Firstly,a chaotic sequence generated through Tent chaos mapping is employed to initialize the population,preventing the problem of premature convergence during the initialization phase.Secondly,by combining the artificial immune algorithm with the binary particle swarm algorithm,crossover and mutation operations on the population individuals are performed.The introduction of antibody concentration and immune operation phases ensures the diversity of the antibody population,enhancing the algorithm's global search capability.The immune memory phase is utilized to store high-quality antibodies,preventing the degradation of the antibody population during the updating process and continually refreshing the population.Then,a partition optimization model for fault location in active distribution networks with distributed energy resources is constructed,reducing the search dimension of fault sections through a two-step dimensionality reduction.The simulation test results demonstrate the significant advantages of the AIBPSO algorithm combined with the exhaustive search method in terms of high accuracy and speed,especially in multi-branch complex distribution networks with single or multiple faults,particularly in scenarios of information distortion within multi-branch distribution networks.

关 键 词：配电网 分布式电源 故障定位 AIBPSO算法 分区优化模型 

分 类 号：TM73[电气工程—电力系统及自动化]