Identification of Cross-Country Fault with High Impedance Syndrome in Transmission Line Using Tunable Q Wavelet Transform

Abstract

The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the risk of cross-country faults (CCFs) and high-impedance fault (HIF) syndrome in these regions. Recognizing and classifying CCFs associated with HIF syndrome is the most challenging part of the project. This study extracted signal characteristics associated with CCF and HIF syndrome using the Tunable Q Wavelet Transform (TQWT). An adaptive tunable Q-factor wavelet transform (TQWT) based feature-extraction approach for CCHIF fault signals with high impact, short response period, and broad resonance frequency bandwidth was presented. In the first part, the time–frequency distribution of the vibration signal is used to determine the distinctive frequency range. Adaptive optimal matching of the impact characteristic components in the vibration signal was achieved by optimizing the number of decomposition layers, quality factor, and redundancy of TQWT based on the characteristic frequency band. In the last, the TQWT inverse transform was utilized to recreate the best sub-band to boost its weak impact characteristics. The effectiveness of the approach is confirmed by simulation and experimental findings in signal processing. The best decomposition level for signature features that can be extracted has been decided by Minimum Description length (MDL). The IEEE 39-bus system is used to test the suggested approach with reactor switching and the Ferranti effect.