Identification of Fault Location in Distribution Networks Using Multi Class Support Vector Machines

Abstract This paper presents a multi-class support vector machine (SVMs) approach for locating and diagnosing faults in electric power distribution feeders. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation and remote terminal units (RTUs). To show the effectiveness of the proposed methodology, a practical 52 nodes, 3 feeder distribution systems (DS) with loads is considered. Practical situations in distribution systems, such as protective devices (circuit breakers/isolators) placed at different locations and all types of faults with a wide range of varying source short circuit (SSC) levels and fault impedances are considered for studies. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted feeder section and the fault impedance. The results demonstrate the feasibility of applying the proposed method in practical distribution automation (DA) system for fault diagnosis.