A novel partial discharge localization method for GIL based on the 3D optical signal irradiance fingerprint and bagging-KELM

Partial discharge (PD) is one of the main reasons that endanger the safe operation of power equipment. The effective detection and localization of PD in a gas-insulated transmission line (GIL) is essential for timely finding the insulation defects to improve maintenance efficiency. Therefore, this paper proposes a method based on the three-dimensional optical signal irradiance fingerprint and bagging-kernel extreme learning machine (Bagging-KELM), which introduces optical simulation into the PD localization. Using a simulation model with the same size and optical sensor arrangement as the experimental GIL, optical simulation signals emitted from different PD sources are collected. Principal component analysis is used to extract the signal features to construct optical PD fingerprints that correspond to PD source locations. This paper builds all the simulated PD fingerprints into a PD fingerprint database. Afterwards, the Bagging-KELM is used to match PD optical fingerprints detected on site with the fingerprint database to identify the location of the PD sources. The experimental results show that the average localization error of this method is 0.93 cm, with 93.75% of the errors being less than 1.5 cm. The proposed method has better performance than the traditional KELM and BPNN, which can achieve precise PD localization in GIL.