Neural Network Analysis for Classification of Defected Ceramic Insulators

Abstract Partial discharge (PD) is a phenomenon that could occur in insulating materials when there is a localized breakdown of the electrical insulation. PD can lead to the formation of small arcs or sparks within the insulating material, which can cause damage and degradation to the insulator over time. In ceramic insulators, there are several factors that can cause PD including manufacturing defects, aging, and exposure to environmental conditions such as moisture and temperature extremes. As a result, detecting and monitoring PD in ceramic insulators is important for ensuring the reliability and safety of electrical systems that rely on these insulators. This can be done using various techniques which can provide information about the location, severity, and frequency of PD events. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques are performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. Moreover, Fourier transform analysis is adopted to be compared to the wavelet transform analysis. Artificial neural network (ANN) has been used to build an intelligent classifier for easily and accurately classification of defective insulators. The overall recognition rate of the classifier was obtained as 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification.