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Monitoring Porcelain Insulator Condition Based on Leakage Current Characteristics.

Ali Ahmed SalemKwan Yiew LauMohd Taufiq IshakZulkurnain Abdul-MalekSamir A Al-GailaniSalem Mgammal Al-AmeriAmmar MohammedAbdulaziz Ali Saleh AlashbiSherif S M Ghoneim
Published in: Materials (Basel, Switzerland) (2022)
Insulator monitoring using leakage current characteristics is essential for predicting an insulator's health. To evaluate the risk of flashover on the porcelain insulator using leakage current, experimental investigation of leakage current indices was carried out. In the first stage of the experiment, the effect of contamination, insoluble deposit density, wetting rate, and uneven distribution pollution were determined on the porcelain insulator under test. Then, based on the laboratory test results, leakage current information in time and frequency characteristics was extracted and employed as assessment indicators for the insulator's health. Six indicators, namely, peak current indicator, phase shift indicator, slope indicator, crest factor indicator, total harmonic distortion indicator, and odd harmonics indicator, are introduced in this work. The obtained results indicated that the proposed indicators had a significant role in evaluating the insulator's health. To evaluate the insulator's health levels based on the extracted indicator values, this work presents the naïve Bayes technique for the classification and prediction of the insulator's health. Finally, the confusion matrix for the experimental and prediction results for each indicator was established to determine the appropriateness of each indicator in determining the insulator's health status.
Keyphrases
  • public health
  • healthcare
  • mental health
  • risk assessment
  • health promotion
  • machine learning
  • heavy metals
  • particulate matter
  • climate change
  • deep learning