摘要
分布式电源(Distributed Generator,DG)的大规模并网引发配电网潮流分布的时空异动,传统故障定位方法的有效性面临严峻挑战。为提升配电网故障点精确定位效率,创新性地提出具有双重自适应机制的改进型二进制粒子群算法(Dual Adaptive Binary Particle Swarm Optimization,DABPSO),通过权重系数动态调节和位置变异自适应控制实现算法性能优化。基于电气与电子工程师协会(Institute of Electrical and Electronics Engineers,IEEE)33节点含DG配电网络构建仿真模型,结果表明:相较于二进制粒子群算法,DABPSO算法在收敛稳定性方面展现出显著优势,有效收敛次数提升率达37.2%,全局收敛平均迭代次数缩短26.8%。分析算法表现可知,DABPSO的收敛特性具有强健壮性,收敛过程不受单相接地、相间短路等故障类型的工况差异影响。
The large-scale grid integration of Distributed Generator(DG)triggers spatial and temporal anomalies in the distribution network current distribution,which poses a serious challenge to the effectiveness of the traditional fault localization methods.In order to improve the efficiency of fault location in distribution networks,this paper innovatively proposes an improved dual adaptive binary particle swarm optimization(DABPSO)with a dual adaptive mechanism,which optimizes the performance of the algorithm through the dynamic adjustment of the weight coefficients and adaptive control of positional variability.Based on the Institute of Electrical and Electronics Engineers(IEEE)33-node DG-containing distribution network,the simulation model is constructed,and the results show that compared with binary particle swarm algorithm,DABPSO shows significant advantages in convergence stability,with an improvement rate of 37.2%in the number of effective convergence,and the average number of iterations of global convergence is shortened by 26.8%.The algorithm analysis shows that the convergence characteristics of DABPSO are robust,and its convergence process is not affected by the differences in fault types such as single-phase grounding and inter-phase short-circuit.
作者
李嘉伟
刘晓琴
陆银红
汪坤坤
高惠芳
LI Jiawei;LIU Xiaoqin;LU Yinhong;WANG Kunkun;GAO Huifang(School of Information and Control Engineering,Liaoning Petrochemical University,Fushun,Liaoning 113001,China)
出处
《智能物联技术》
2025年第2期49-54,共6页
Technology of Io T& AI
基金
辽宁省教育厅重点攻关项目(JYTZD2023153)。
关键词
故障定位
粒子群算法
分布式电源(DG)
收敛特性
fault location
particle swarm optimization algorithm
Distributed Power(DG)
convergence characteristics
