Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.