Automatic Blind Equalization and Thresholding for Partial Discharge Measurement in Power Transformer

Partial discharge (PD) signals acquired from on-line measurements of power transformers are easily overwhelmed by various interference and noise. This paper proposes an automatic blind equalization (BE) and morphological thresholding method for PD signal de-noising. Firstly, BE automatically selects an equalized signal that reveals PD impulses from an acquired noise-corrupted signal. Then, automatic morphological thresholding (AMT) is adopted for determining thresholds on the equalized signal. After de-noising with BE and AMT, phase-resolved pulse sequence (PRPS) is constructed and used for analyzing the types of insulation defects that cause discharges. To verify the proposed method, PD measurements on experimental PD models and a distribution transformer have been conducted. The results show that PD impulses can be extracted from severely noise-corrupted signals by using the proposed method. Also, PRPS constructed from de-noised signals can achieve consistency in revealing the types of insulation defects even different types of PD sensors and measurement systems are used.

I. Introduction

Partial discharge (PD) measurement is one of the commonly adopted methods for on-line monitoring and assessing insulation systems of power transformers. However, a variety of environmental interference and noise could be imposed on the signals, which are acquired by PD sensors. These interference and noise can be generated from diverse sources and exhibit different distribution properties, such as sinusoidal, periodic, Gaussian, and stochastic behaviors [1], [2].