Xiaowei Wang

The focus of the paper is the difficulty of high impedance fault (HIF) detection in distribution network, and its ease to be confused with capacitor switching (CS) and load switching (LS). Based on the intermittent reignition and extinction characteristics of HIF current, this paper proposes a novel HIF detection method, which combines variational mode decomposition (VMD) and Teager-Kaiser energy operators (TKEOs). The HIF detection method is as follows: First, perform the VMD on transient zero sequence currents to obtain the intrinsic mode functions (IMFs) and select the IMFs with the largest kurtosis value as the characteristic IMFs. Second, calculate the characteristic IMFs to obtain TKEOs and divide into subintervals of TKEOs waveform to calculate the time entropy values. Finally, construct HIF detection criterion as follows: when time entropy value is 0, it is judged as CS or LS. When the entropy value is not 0, it is judged as HIF. A large number of simulations and field data tests show that the method is accurate and stable, and under the interference of 1 dB strong noise, it can accurately judge. Compared with other methods, the method has higher feature extraction accuracy, less calculation time, and better judgment accuracy.

High-impedance faults (HIFs) pose the greatest challenge for distribution system protection, especially for microgrids and distribution networks with distributed generators (DGs) that have flexible operation modes. This paper analyzes the faulty features of HIFs and proposes a HIF detection method that uses empirical wavelet transform (EWT) and differential faulty energy. The proposed method is as follows. First, the various time-frequency components are obtained by utilizing the EWT to decompose the differential faulty energy and adaptively select the feature component with the largest permutation entropy. Second, the permutation variance index is constructed based on the sample point number and feature component energy, and then it is employed to detect HIFs. Finally, low voltage microgrid simulation tests, medium voltage distribution system integrated by DG simulation tests, and field tests show that the proposed method can correctly distinguish HIFs from normal disturbances, including operation mode switches, load switches, capacitor switches, and DG switches. The advantages of the proposed method are also elaborated in detail, from signal preprocessing and feature extraction.

In the event of a single line to ground (SLG) fault in a non-effectively grounded distribution network, the faulted current is weak (only a few amperes or less) and the existing devices cannot accurately judge the faulted feeder. In this paper, we proposed algorithms that combine complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert transform to construct a multi-criteria comprehensive voting method. First, CEEMDAN algorithm is used to decompose the zero-sequence current to obtain the IMF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (the first intrinsic mode function) component and the Hilbert transform is used to calculate the instantaneous amplitude and instantaneous phase. Then, according to the three largest instantaneous amplitudes information, we constructed the characteristic instantaneous phase, characteristic instantaneous energy relative entropy and characteristic instantaneous zero sequence current polarity criteria from the phase, energy and polarity, respectively. Finally, we proposed a comprehensive voting method, which is specifically shown as follows: when two or more criteria show that one feeder or the bus has an SLG fault, it is voted that the feeder or the bus has an SLG fault. In contrast, if the judgment results of the three criteria are inconsistent, then we would return to recalculation and then vote. Compared with existing method, simulation tests and field experiments show that the method proposed in this paper has higher accuracy and a faster calculation speed.