Fault Section Identification in Smart Distribution Systems Using Multi-Source Data Based on Fuzzy Petri Nets

This paper proposes a hybrid fault location method for smart distribution systems by using available multi-source data, including data from protective devices, fault indicators (FIs), smart meters, voltage and current meters, and computer simulations. A fault diagnosis model based on fuzzy Petri net (FPN) technique is developed that employs discrete evidences, such as status of protective devices and FIs to estimate the faulted section. To eliminate the possible false fault section estimations and pinpoint the actual location of the fault a mismatch index is defined. It quantifies the similarity between recorded measurements, such as voltage and current signals during faults and the corresponding values obtained by performing short circuit analysis. The fault scenario with minimum mismatch degree is determined as the actual location of the fault. The proposed method can precisely determine the fault location in distribution systems with many laterals and sub-laterals with heterogenous line parameters in the presence of distributed generators (DGs). The accuracy and effectiveness of the proposed method are validated by applying the proposed method to the simulated IEEE 33 node feeder distribution system.