Multifractal Spectrum Identification of Partial Discharge in GIS Based on Optical Detection

Abstract

Abstract Partial discharge (PD) pattern recognition is an effective means to accurately obtain the correspondence between PD features and insulation defects. Optical detection for PD has strong anti-interference ability. Therefore, this paper carried out research on the pattern recognition of PD optical signal. In this paper, the PD optical signal under four typical defects of gas-insulated switchgears (GIS) are obtained by fluorescent fiber sensing system, and the four kinds of grayscale images are established. The multifractal spectrum features of the grayscale images of the optical signals are extracted. By analysing the physical meaning of each feature information in the multifractal spectrum, the feature components for PD pattern recognition are extracted, and the improved conjugate gradient back propagation (BP) neural network algorithm is selected as the classifier. This paper show that the multifractal spectrum can effectively describe the degree of unevenness of PD grayscale images and the geometric features of different level. The correct recognition rate of all kinds of defects by the multifractal spectrum is more than 87%, which is better than the identification of the box dimension and information dimension as the feature components. It lays the foundation for the future application of the optical detection.