A detection method of high impedance arcing fault for distribution network with distributed generation based on CEEMDAN and TEO algorithm

The vigorous development of renewable energy has induced multiple sources connected to regional distribution networks. However, the high impedance ground fault that appeared in the distribution network can induce an intermittent arc accompanied by extinction and reignition, which may damage renewable energy generation devices severely. Therefore, the detection of arcing faults is profoundly necessary for practice, but the high impedance arcing fault is easily confused with switches of motor or capacitor bank. In order to solve high impedance arcing fault detection, this paper proposes a high impedance arcing fault detection method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN algorithm is used to extract the intrinsic mode function (IMF) of transient zero-sequence current (TZSC). According to the correlation coefficient and waveform similarity, which can select maximum correlation coefficient value as the characteristic component of TZSC; Then, TEO is utilized to track the change of signal in real-time, meanwhile, wavelet singular entropy (WSE) is applied to calculate the characteristic component of TZSC. In particular, high impedance arcing fault (HIAF), motor switching (MS), and capacitor bank switching (CBS) can be effectively identified by comparing WSE. The effectiveness of CEEMDAN-TEO detection method is verified by simulation, which owns advantages in extraction accuracy, calculation efficiency, and detection accuracy of feature components.