电力系统及其自动化学报2017，Vol.29Issue(11)：99-104,6.DOI:10.3969/j.issn.1003-8930.2017.11.016
基于变异粒子群优化-禁忌搜索混合算法的配电网状态估计
Distribution State Estimation Based on Mutation Particle Swarm Optimization-tabu Search
孟志强 1覃仕樾 1蔡航1
作者信息
- 1. 湖南大学电气与信息工程学院,长沙 410082
- 折叠
摘要
Abstract
To solve the optimization problem of distribution state estimation with nonlinear equipments,a state estima?tion model for the distribution network with distributed generations is established by adopting the node load values and distributed generation output as state variables,and a hybrid algorithm of mutation particle swarm optimization-tabu search is proposed to solve the established model. The proposed algorithm increases the particle diversity by mutating in?dividual extremums;meanwhile,to improve the search ability of particle swarm optimization algorithm in the latter stage and avoid premature convergence,tabu search algorithm is used in the latter stage of iterations. An IEEE 33-node distribution system is used as a simulation example,and simulation results show that the proposed algorithm can esti?mate the node load values and distributed generation output effectively,and the maximum individual relative error and maximum individual absolute error of state estimations are much smaller than those of ant colony algorithm ,particle swarm optimization algorithm and genetic algorithm.关键词
配电网/状态估计/粒子群优化/禁忌搜索/变异操作Key words
distribute network/state estimation/particle swarm optimization/tabu search/mutation operation分类
信息技术与安全科学引用本文复制引用
孟志强,覃仕樾,蔡航..基于变异粒子群优化-禁忌搜索混合算法的配电网状态估计[J].电力系统及其自动化学报,2017,29(11):99-104,6.
