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-f...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(μPMU)placement.In the second stage,the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data inμPMUs in a high-noise environment.l2,1 principal component analysis(PCA),i.e.,PCA based on the l2,1 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.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2017YFB0902800)Science and Technology Project of the State Grid Corporation of China(No.52094017003D)。
文摘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(μPMU)placement.In the second stage,the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data inμPMUs in a high-noise environment.l2,1 principal component analysis(PCA),i.e.,PCA based on the l2,1 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.
