Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.
