Fault location of distribution networks based on multi-source information

Abstract

In order to promote the development of the Internet of Things (loT), there has been an increase in the coverage of the customer electric information acquisition system (CEIAS). The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit (FTU) and the fault tolerance rate is low when the information is omitted or misreported. Therefore, this study considers the influence of the distributed generations (DGs) for the distribution network. This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm (BPSO). The improved Dempster/S-hafer evidence theory (D-S evidence theory) is used for evidence fusion to achieve the fault section location for the distribution network. An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.