A D-S evidence theory fault diagnosis method based on MapReduce for SF6 high voltage switchgear

As one of the AC and DC switching devices in power system, high voltage switchgear is mainly used for the control and protection of power systems. In order to meet the demand of large amount of data, many types, fast processing speed and high quality of fault diagnosis in large data environment, a parallel processing framework based on a data fusion fault diagnosis algorithm designed by D-S Evidence Theory is introduced, taking SF6 high voltage circuit breaker as an example in this paper. SF6 high voltage circuit breaker trip(closing) coil current, voltage and current time are selected as input of the diagnosis system, and six main fault types are selected as output of the diagnosis system in this paper. Considering the multi-level, multi-layer and multi-faceted advantages of multi-sensor data fusion, D-S evidence theory based on MapReduce framework is designed. The simulation shows that the requirements of mass rapid diagnosis of high voltage switch equipment can be satisfied. Compared with the traditional serial processing method, processing time can be reduced by 95 percent under situation of hundreds of megabytes data.