• Prof.Vaibhav Ashok Ghodeswar
• Dr.Mirza Ansar Beg
• Miss Deepmala.S. Tayade

Accurately recognizing and categorizing faults is critical for ensuring a power system's dependability and stability. In a fault condition, the tripping action is heavily influenced by the voltage and current waveforms recorded at the relay position. As a result, a quick and precise analysis is necessary to find and characterize problems in power transmission lines. Various signal processing algorithms are employed to examine the voltage and current waveforms of defect signals. Using discrete wavelet-based signal analysis, this study seeks to detect and classify several forms of transmission line problems. The defect can be classified by inspecting the current waveform of the fault signals with the discrete wavelet transform. The discrete approximation coefficient of the wavelet function is employed as an indicator for defect identification and classification. The artificial neural network is used to classify different transmission line defects

MATLAB simulink ,Wavelet Transform ,ANN ,Confusion matrix

"Wavelet-Based Transmission Line Fault Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.c33-c39, November-2023, Available :http://www.jetir.org/papers/JETIR2311207.pdf

"Wavelet-Based Transmission Line Fault Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppc33-c39, November-2023, Available at : http://www.jetir.org/papers/JETIR2311207.pdf