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

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