Monitoring Porcelain Insulator Condition Based on Leakage Current Characteristics.

Area of Science:

• Electrical Engineering
• Materials Science

Background:

• Insulator monitoring is vital for power system reliability.
• Leakage current analysis is a key method for assessing insulator health.
• Flashover risk on porcelain insulators necessitates accurate health prediction.

Purpose of the Study:

• To investigate leakage current characteristics for evaluating porcelain insulator flashover risk.
• To introduce novel leakage current indices for insulator health assessment.
• To develop a predictive model for insulator health status using machine learning.

Main Methods:

• Experimental investigation of porcelain insulators under various contamination and wetting conditions.
• Extraction of time and frequency domain leakage current characteristics.
• Introduction and application of six novel leakage current indicators.
• Classification and prediction of insulator health using the Naïve Bayes technique.

Main Results:

• Contamination, wetting rate, and pollution distribution significantly affect leakage current.
• The six introduced indicators demonstrate a significant role in evaluating insulator health.
• The Naïve Bayes classifier effectively predicted insulator health status based on the indicators.

Conclusions:

• Novel leakage current indicators provide valuable insights into porcelain insulator health.
• The Naïve Bayes technique offers a reliable method for predicting insulator health status.
• Accurate insulator health monitoring enhances power system operational safety and reliability.