Error correction method based on multiple neural networks for UHF partial discharge localization

The time difference method based ultra-high-frequency (UHF) partial discharge (PD) localization has not been extensively applied in substations due to its excessive localization error. A novel and effective PD localization error correction method, consisting of two multiple radial basis function (RBF) networks, RBFtfor time delay error correction and RBFpfor PD position error correction, is reported in this paper as a solution. Furthermore, the RBFt, trained by time delay value measured in field experiments and RBFp, trained by the localization results corrected by RBFt, have both adopted the multiple neural networks structure which could further improve their error correction ability. The field tests proved that, the error correction networks RBFtand RBFphave strong ability to learn and simulate the error distribution. After the error correction and compensation, the PD distance localization error is reduced from 3m to 0.5m and the direction angle error is reduced from 12° to less than 5°. Universality experiments also revealed that the proposed method has a good adaptability for different time-delay algorithms and different types of PD models, which proved the robustness and effectiveness of proposed method.