GIS partial discharge pattern recognition based on the chaos theory

Abstract

Partial discharge (PD) is a key parameter which describes insulation condition of gas-insulated switchgear GIS, GIS internal defects can be discovered in time with the interferences such as corona discharge from the power system using Ultra-high frequency (UHF) method. Traditional method based on statistical characteristics is limited to the analysis of image features that describe the PD pattern plot, causing low recognition rate of some kinds of PD. In this paper, the acquisition of the PD characteristics caused by four typical insulation defects forms φ-v-n 3D PRPD pattern plots sample matrix. The largest Lyapunov exponent of each column of matrix is calculated. A 36-dimension vector is then obtained as the chaotic characteristics of the PD in different voltage phases. The experimental results show that the recognition method based on the chaotic characteristics performs well on all four kinds of insulation defects and can satisfy the recognition order. The method based on the chaotic characteristics has a strong recognition ability for the discharge physical models of gas gap, which is an advantage over the traditional method. The two recognition methods have a good complementary property. Combining the complementary chaotic and statistical characteristics in a decision-making level by using the Dempster-Shafer evidence theory results in an accuracy of above 98%.