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Abstract <p>An insulator is one of the crucial parts of the high-voltage overhead cables. Leakage currents may circulate on the insulator’s surface as a result of external factors and contaminants adhered to the surface. Large leakage currents (LC) have the potential to produce flashover, heat losses and surface damage to the insulator. In order to prevent early flashover, this research offers a&#xa0;method that measures the severity of the insulator surface using harmonic measurements of leakage currents. In this work, on 11 kV polymer insulators, the leakage currents were assessed at various equivalent soluble deposit density (ESDD) levels. Discrete Wavelet transform topology is incorporated to extract features from the LC signal. Random forest (RF) with fuzzy inference system (FIS) using a diverse range of LC signals collected over an extended period is incorporated. From results, it is evident that the suggested pollution severity classifier, is highly effective with an accuracy about 95%. Its implementation holds great promise for electrical utilities.</p>

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Optimized Hybrid Approach for Assessing Pollution Severity in Polymer Insulators Using Random Forest and Fuzzy Logic

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Kannan Kandavelu,
  • Sivakumar Sivagnanam,
  • Senthilkumar Angappan,
  • Manikumar Thangaraj,
  • Sangeetha Balashanmugam,
  • Meenakshi Sundaram Padamanabhan,
  • Moniya Pathrakalimuthu

摘要

Abstract

An insulator is one of the crucial parts of the high-voltage overhead cables. Leakage currents may circulate on the insulator’s surface as a result of external factors and contaminants adhered to the surface. Large leakage currents (LC) have the potential to produce flashover, heat losses and surface damage to the insulator. In order to prevent early flashover, this research offers a method that measures the severity of the insulator surface using harmonic measurements of leakage currents. In this work, on 11 kV polymer insulators, the leakage currents were assessed at various equivalent soluble deposit density (ESDD) levels. Discrete Wavelet transform topology is incorporated to extract features from the LC signal. Random forest (RF) with fuzzy inference system (FIS) using a diverse range of LC signals collected over an extended period is incorporated. From results, it is evident that the suggested pollution severity classifier, is highly effective with an accuracy about 95%. Its implementation holds great promise for electrical utilities.