Research on Automated Diagnosis and Measurement of XLPE Cable Joint Faults Based on Improved XGBoost Algorithm
Research on an automated fault diagnosis method for XLPE cable joints based on an improved XGBoost algorithm.Taking the 35 kV XLPE cable joint as an example,design a partial discharge simulation experiment to measure four types of insulation fault partial discharge signals and generate a two-dimensional partial discharge map.Extract fault features that describe the differences in projection shape and positive and negative half circumference contours from them,construct one-dimensional vector input XGBoost model,and achieve automated fault diagnosis.Ap-plying the Harris hawks optimization to optimize model parameters and improve diagnostic classification performance.The experimental results show that this method can effectively measure partial discharge spectra of different types of faults and achieve high-precision automatic diagnosis of XLPE cable joint faults based on their characteristics,ensur-ing long-term stable operation of cables and better ensuring power safety.
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