LAUSR

Localization of partial discharge (PD) is a reliable and necessary technique for early prediction of impending failure in transformer windings. Different PD sources have diverse impacts on insulation, and PDs measured at different locations exhibit unique energy characteristics. An approach based on the energy characteristics of the PD signal is proposed here to locate PD inside the winding. An electrical detection method has been considered with PD pulses measured at the top and bottom of the winding through high-frequency current transformers (HFCTs). To reveal multiscale intrinsic characteristics of the PD signal, authors extracted intrinsic mode function (IMF) using ensemble empirical mode decomposition (EEMD). Next, the instantaneous energy distribution-permutation entropy (PE-IED) values of the first several IMFs were calculated to capture the energy of the PD. Then, dimensionality reduction was ensured by the fast principal component analysis that uses the fixed-point algorithm method. Finally, adaptive density-based clustering using the nearest neighbor graph (ADBSCAN-NNG) approach is proposed to identify the local high-density energy components. The proposed method is verified using the winding of a vacuum cast coil transformer and the localization results reveal the feasibility of the proposed method in locating PDs over existing localization algorithms. The reduced complexity of the proposed algorithm ensures the accurate onsite PD localization in transformer windings.