摘要
为提高主动配电网故障恢复的快速性和可靠性,提出一种基于变异粒子群算法的恢复策略。光储系统与负荷特性模型的构建是研究策略的基础,利用光储模型保证负荷可靠恢复,在构建负荷特性模型时考虑负荷时变性、需求时变性及负荷可控性的特点。在建立的光储系统与负荷特性模型基础上研究故障恢复策略,首先对配电网进行动态孤岛划分,利用光储系统对孤岛内负荷进行可靠恢复,保证用户侧需求度高的负荷优先恢复,然后以总失电负荷最少、网损最小及开关动作次数最少为综合目标函数,运用变异粒子群算法得到孤岛与主网配合的配电网综合恢复策略,提高了主动配电网可靠性。最后,采用IEEE 33节点系统进行算例分析,结果验证了模型与恢复策略的优越性。
In order to improve the rapidity and reliability of fault recovery in the active distribution network,a fault recovery strategy based on mutation particle swarm optimization algorithm is proposed.The construction of optical storage system and load characteristic model is the basis of the research strategy.The optical storage model is used to ensure reliable load recovery.The load characteristic model is constructed according to the time-varying characteristics of the load and user-side demand while considering the controllable load.Based on the optical storage system and load characteristic model,the fault recovery strategy is studied.Firstly,the distribution network is dynamically islanded,and the optical storage is used to reliably restore the load in the island to ensure that the load with high user-side demand is restored first.With the least total power-loss load,the least power loss and the least number of switching operations as the comprehensive objective function,the mutation particle swarm optimization algorithm is used to obtain a comprehensive fault recovery strategy for the distribution network that cooperates with the island and the main network,which improves the reliability of the active distribution network.Finally,taking IEEE 33-bus system as a simulation example,the results verify the superiority of the model and the recovery strategy.
作者
徐岩
张荟
孙易洲
XU Yan;ZHANG Hui;SUN Yizhou(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Baoding 071003,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2021年第12期45-53,共9页
Electric Power Automation Equipment
基金
河北省重点研发计划项目(20314301D)。
关键词
主动配电网
孤岛划分
故障恢复
光储系统
变异粒子群算法
active distribution network
island partition
fault recovery
optical storage system
mutation particle swarm optimization algorithm
