Partial discharge analysis using PCA and ANN for the estimation of size and position of metallic particle adhering to spacer in Gas-Insulated System

The presence of metallic particles can adversely affect the reliability of Gas-Insulated Substation (GIS) by initiating partial discharges (PDs). Therefore, the investigation of PD characteristics and particle size and position on the spacer surface are the significant steps toward the reliability improvement of the GIS equipments. This paper presents the use of Back-Propagation Artificial Neural Network (BP-ANN) technique supplemented with Principal Component Analysis (PCA) as the PD pattern recognition tools for the estimation of the particle size (length) and position on the spacer surface in a simulated GIS. PD features acquisition was performed by collecting their fingerprints from the measurements carried out using IEC 60270 method. The role of PCA is to reduce the dimension of the collected PD fingerprint data. The obtained results show that PCA can significantly improve the BP-ANN performance in terms of execution time. Without PCA, 88% and 92% accuracies can be achieved when BP-ANN was implemented with 1 and 2 hidden layers, respectively. With the integration of PCA, execution times were greatly reduced while retaining fairly high accuracy, i.e. 88% and 88%. Thus the proposed method is a contribution in development of the tool for improving the reliability of GIS.