Denoising Autoencoder for Partial Discharge Identification in Instrument Transformers

Abstract

Analyzing Partial Discharge (PD) signals is crucial to assessing the health of insulation in high-voltage systems. Nevertheless, noise often distorts these signals, hindering the ability to obtain precise information. This paper proposes a novel deep-learning approach using two denoising autoencoders (DAEs) to learn data representations and eliminate noise during reconstruction. By leveraging DAEs' capacity to capture essential features within the latent space, this method enhances the analysis of PD signals and yields more accurate results. This paper investigates the effectiveness of two deep-learning architectures for denoising partial discharge signals in high-voltage insulation systems. Experimental results carried out on a PD dataset demonstrated the efficiency of the Linear AE model in removing noise in sets A, B, and C suggesting that DAEs hold great promise in PD signal denoising.