PCA-Enhanced Methodology for the Identification of Partial Discharge Locations

Abstract

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly.

Keywords

• partial discharge
• principal component analysis
• K-nearest neighbour
• localization
• fingerprint