Prediction of the Levels of Contamination of HV Insulators Using Image Linear Algebraic Features and Neural Networks

Abstract

Contaminated high-voltage (HV) insulators in polluted areas may lead to flashovers if they are not cleaned periodically. Flashover often leads to lengthy service outages and thus has a considerable impact on power system reliability. Therefore, an accurate prediction of the contamination level of HV insulators is vital. In this study, a MATLABbased algorithm for predicting the contamination level is proposed. The algorithm uses the extracted features (in this work, linear algebraic features) from images captured by digital cameras as an input to a neural network. When compared to existing methods reported in the literature, the designed neural network correlates successfully the captured insulator images and the contamination level when tested on unseen insulators.

Performance Evaluation of 230 kV Polymer Insulators in the Coastal Area of Saudi Arabia

“…Using the test procedures, electrical testing has been conducted for the field insulator samples at KHVL [1]. the test procedures shown in Fig.…”

“…High voltage (HV) insulators are essentially used to provide the electrical insulation and mechanical support of a transmission lines hardware. The electrical performance of the composite insulators is normally affected by the environmental conditions such as wind speed, wind direction, rainfall, relative humidity, ultraviolet (UV) radiation, ambient temperature, adhesive soluble contaminants, and moisture contents [1]. The source and degree of the contamination, wind direction, particles adhesiveness, and insulator design can be considered as selfcleaning ways [1].…”

Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

“…Among the numerous investigations devoted to the polluted insulators monitoring field, just a few number of studies dealing with image processing techniques. For instance, Maraaba et al [18] elaborated multi-layer FNN (MFNN) to predict the contamination level. To achieve this goal, ten features have been extracted from HV insulator images and then used as inputs of the network.…”

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

“…Artificial neural network (ANN) model was presented in [13][14][15]. In [6], the support vector models were introduced.…”