High-impedance Fault Detection Method Based on Feature Extraction and Synchronous Data Divergence Discrimination in Distribution Networks

High-impedance faults (HIFs) in distribution networks may result in fires or electric shocks. However, considerable difficulties exist in HIF detection due to low-resolution measurements and the considerably weaker time-frequency characteristics. This paper presents a novel HIF detection method using synchronized current information. The method consists of two stages. In the first stage, joint key characteristics of the system are extracted with the minimal system prior knowledge to identify the global optimal micro-phase measurement unit (mu PMU) placement. In the second stage, the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data in mu PMUs in a high-noise environment. l(2,1) principal component analysis (PCA), i.e., PCA based on the l(2,1) norm, is applied to an extracted system state and fault features derived from different resolution data in both stages. An economic observability index and HIF criteria are employed to evaluate the performance of placement method and to identify HIFs. Simulation results show that the method can reliably detect HIFs with reasonable detection accuracy in noisy environments.