摘要
针对传统配电网故障定位算法仅考虑配电网馈线终端单元作为单一信息源存在容错率较低的问题,利用用户用电采集系统的供电信息作为冗余信息,考虑分布式电源接入配电网的影响构建故障定位适应度函数,并通过二进制粒子群算法求解模型.利用改进D-S证据理论进行证据融合,根据证据决策准则得到配电网故障区段定位结果.仿真结果表明,该算法可有效实现故障定位,并且在单重故障与双重故障下FTU信息有误时,较传统算法相比容错率有所提高.
Aiming at the problem that the traditional fault location algorithm only considers feeder terminal unit(FTU)as a single information source,which causes a lower fault tolerance rate,the power supply information of electricity collection system for users was used as redundant information,and fault location fitness functions were constructed by considering the influence of distribution generations in distribution network.The model was solved by binary particle swarm optimization algorithm,and the improved D-S evidence theory was employed for evidence fusion.In addition,the fault location results were acquired according to the evidence decision criteria.The simulation results show that the as-proposed algorithm can effectively identify fault location,and its fault tolerance rate is improved compared with that of traditional algorithm when the information of FTU is mistaken in the case of single or double faults.
作者
于安迎
程云祥
卢芳
李嘉媚
刘旭
YU An-ying;CHENG Yun-xiang;LU Fang;LI Jia-mei;LIU Xu(Rizhao Power Supply Company, Shandong Electric Power Company Limited, Rizhao 276826, China;School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
出处
《沈阳工业大学学报》
EI
CAS
北大核心
2020年第4期373-378,共6页
Journal of Shenyang University of Technology
基金
国家自然科学基金项目(U1866206)
国网山东省电力公司科技项目(52061718004F)。
关键词
泛在电力物联网
多源信息
D-S证据理论
粒子群算法
容错率
故障定位
分布式电源
配电网
ubiquitous power Internet of Things
multi-source information
D-S evidence theory
particle swarm optimization algorithm
fault tolerance rate
fault location
distribution generation
distribution network
