An ANFIS-based fault classification approach in power distribution system

Fault classification is very important for power system operation because it is the premise of fault analysis process. In this paper, an ANFIS (Adaptive Neural Fuzzy Inference System) based fault classification scheme in neutral non-effectively grounded distribution system is proposed. The transient currents are obtained by wavelet transform after faults occur. According to the statistic characteristic of transient currents in different fault types, the fault identifiers are defined. The fault identifiers can characterize the traits of fault type and show different disciplinarian in different fault types. They are inputted into three ANFISs to obtain the fault type. The proposed approach only needs the voltages and currents measured at substation, and can identify ten types of short-circuit fault accurately. The simulation model is established in PSCAD/EMTDC environment, and the performance of the proposed approach is studied. The results show that it has high accuracy. Besides, the adaptability of proposed approach to the neutral compensated grounding system, different network configurations and so on are verified through simulation. Through simulation, the proposed approach exhibits good performance.

► Fault identifiers based on statistics are constructed to characterize fault types. ► The structure of fault classifier utilizing ANFISs is carefully designed. ► The classification results are highly accurate from simulation study. ► The adaptability is very good under different conditions (e.g., arc fault).