Wavelet Transform Technique for Denoising of UHF PD Signals in GIS

Abstract

Reliable operation of HV equipment in gas insulated substations (GIS) is undermined by insulation defects and particle presence. Partial discharge (PD) monitoring is one of the most effective techniques for insulation condition assessment of HV power apparatus. However, on-line PD measurements are affected by high levels of electromagnetic interference (EMI) that makes sensitive PD detection very difficult. Partial discharge monitoring system by UHF method is suitable for internal condition diagnosis of GIS due to its high sensitivity. However, interferences from noise sources such as corona and radio-frequency noise can affect the signal captured. Recovery of the PD signal by de-noising without degradation can be carried out through the application of wavelet transform by choosing the correct member of the wavelet family. Use of wavelet transform technique offers many advantages over conventional signal processing techniques such as filters and is ideally suited to process transients in high voltage testing and measurements. In this paper PD pulse extraction from noise by wavelet transform analysis through the choice of optimum wavelet transform is investigated. PD pulses and the noisy on-site testing environment were simulated through Gaussian white noise for applying the technique.