基于差分粒子群算法的配电网状态估计  

Distribution Network State Estimation Based on Differential Particle Swarm Optimization

作　　者：唐正国 Tang Zhengguo(Guangzhou Youzhi Electric Technology Co.,Ltd.,Guangzhou Guangdong 510000,China)

机构地区：[1]广州友智电气技术有限公司,广东广州510000

出　　处：《山西电子技术》2024年第5期14-17,共4页Shanxi Electronic Technology

摘　　要：为了对配电网运行状态进行更准确的估计,提出了一种基于DEPSO算法的配电网估算方法。采用差分进化思想对PSO算法的寻优策略进行改进,得到优化性能更好的DEPSO算法,采用IEEE33节点系统进行仿真分析,并与差分算法和粒子群算法进行对比,结果表明,DEPSO算法对电压和电流的估计结果的平均相对误差分别为1.26%和0.79%,相比其他方法的配电网状态估计结果更准确,验证了本文所提配电网估计方法的正确性和实用性。In order to estimate the operating state of distribution network more accurately,a distribution network estimation method based on DEPSO algorithm is proposed.The optimization strategy of PSO algorithm is improved by using the idea of differential evolution,and DEPSO algorithm with better optimization performance is obtained.The IEEE33-node system is simulated and analyzed,and compared with the differential algorithm and particle swarm algorithm.The results show that,the average relative error of the estimation results of voltage and current by DEPSO algorithm is 1.26%and 0.79%respectively,which is more accurate than the estimation results of distribution network state by other methods,which verifies the correctness and practicability of the proposed distribution network estimation method.

关 键 词：配电网 状态估计 差分粒子群算法 量测数据 相对误差 

分 类 号：TM73[电气工程—电力系统及自动化]