Impulsive noise reduction for transient Earth voltage-based partial discharge using Wavelet-entropy

Abstract

Partial discharge (PD) is caused by the localized electrical field intensification in insulating materials. Early detection and accurate measurement of PD are very important for preventing premature failure of the insulating material. Detection of PDs in metal-clad apparatus through the Transient Earth Voltage (TEV) method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment remains the major barrier to improving its measurement accuracy. In this paper, a wavelet-entropy-based PD de-noising method has been proposed. The unique features of PD are characterized by combining wavelet analysis that reveals the local features and entropy that measures the disorder. With such features, a feed-forward back-propagation Artificial Neural Network (ANN) is adopted to recognize the actual PDs from noisy background. Comparing with other methods such as the energy-based method and the similarity-comparing method, the proposed wavelet-entropy-based method is more effective in PD signal de-noising.