Identifying single-phase to ground fault based on line multi-dimensional data in cyber-physical distribution system

Abstract

Single-phase to ground fault is the most common line fault in power distribution systems, which may result in an electrical fire and personal safety problems. Particularly, it is more difficult to identify the fault occurring in the system in which the neutral point is grounded via Petersen coil. This paper proposes a new scheme based on line multi-dimensional data in cyber-physical distribution system, where line multi-dimensional data is used to extract the fault characteristics and thus to identify the faulty line. First, the circuit is analyzed and the difference-like algorithm is used to extract the current signal difference value from the line multi-dimensional data as the feature quantity. In order to reduce the impact of the measurement error on the result, the authors employ the deep neural networks to process the line multi-dimensional data, and demonstrate that this method can effectively resist the error interference. A simulation based on a previously studied model has been carried out and the results show the superiority compared to the fifth harmonic identification method. In addition, the test results obtained in the Dynamic Simulation Laboratory of Huazhong University of Science and Technology also demonstrate the practicality of the scheme.