摘要
随着大量分布式电源和随机负荷等引入,导致配电系统在越来越复杂及不确定的工况中运行,其状态估计结果与量测装置需要更多地考虑不确定性因素。虽然同步相量量测装置可以通过提供实时量测数据来提高状态估计结果,但考虑到配电网节点较多以及投资成本不足造成供需不平衡,所以在短时间内配电网不可能大规模配置。该文基于经济性的原则来配置PMU测量装置,对最大提高状态估计结果的精度进行研究。同时采用改进的磷虾群算法对IEEE-33节点模型进行测试,从而得到PMU最优配置Pareto非劣解集。该文对IEEE-33节点三相配电网系统进行仿真计算,与遗传算法和粒子群算法比较;仿真结果验证了该算法的可行性、有效性以及优越性。
With access of a large amount of distributed power sources and random loads,the distribution system is operating in increasingly complex and uncertain conditions,and its state estimation results and measuring devices need to consider more uncertain factors.Though the phasor measurement units can provide real-time measuring data to improve the results of the state estimation,considering the imbalance between supply and demand caused by the large number of distribution network nodes and the lack of investment cost,it is impossible to configure the distribution network on a large scale in a short time.In this paper,the PMU measuring device is configured on the basis of economy to study the maximum improvement of the accuracy of state estimation results.At the same time,the improved krill swarm algorithm is used to test the IEEE-33 node model,and the Pareto non-inferior solution set of PMU optimal configuration is obtained.In the paper,the three-phase distribution network system of IEEE-33 bus is simulated and compared with the genetic algorithm and the particle swarm optimization algorithm,and the simulation results have verified the feasibility,effectiveness and superiority of the algorithm.
作者
曹鹏
刘敏
杭鲁庆
CAO Peng;LIU Min;HANG Luqing(School of Electrical Engineering,Guizhou University,Guiyang 550025,Guizhou,China)
出处
《电网与清洁能源》
北大核心
2022年第4期61-67,共7页
Power System and Clean Energy
基金
国家自然科学基金项目资助(51967004)。
关键词
配电网
状态估计
同步相量测量装置
改进磷虾群算法
优化配置
distribution network
state estimation
phasor measurement units
improved krill herd algorithm
optimal configuration
