Wavelet transform and fuzzy reasoning for underground power cable fault diagnosis

Traditional fault diagnosis methods, such as Time-Domain Reflectometry and Frequency-Domain Reflectometry, often struggle to handle complex fault signals and have limitations in accuracy and real-time performance.This research aims to develop a more effective cable fault diagnosis model that combines wavelet transform and fuzzy reasoning to improve detection accuracy and real-time performance.The proposed model uses wavelet transform for multi-scale decomposition of fault signals, extracting high-frequency and lowfrequency features, while the fuzzy reasoning system classifies and diagnoses the fault signals based on a preset rule base.Experimental results show that the model achieves high accuracy in identifying various fault types, including short circuit, grounding, open circuit, and partial discharge, with a short circuit fault accuracy of 94.5% and an average diagnosis time of 0.8 seconds.The model also demonstrates strong robustness under noise interference, maintaining over 90% classification accuracy even at a noise intensity of 20 dB.Compared to traditional methods, the model excels in handling complex faults and multiple signals while maintaining high noise resistance.Future research will focus on enhancing real-time performance, improving rule base design, and expanding the model's ability to handle multi-fault scenarios.