Application of wavelet analysis to the determination of partial discharge location in multiple-α transformer windings

详细信息    查看全文

• 作者：Mohammad S. Naderi ; T.R. Blackburn ; B.T. Phung ; Mehdi S. Naderi ; A. Nasiri
• 关键词：Partial discharge ; PD location ; Power transformer ; Signal de-noising ; Signal processing ; Wavelet transform
• 刊名：Electric Power Systems Research
• 出版年：2008
• 出版时间：February 2008
• 年：2008
• 卷：78
• 期：2
• 页码：202-208
• 全文大小：1356 K

文摘

The inner insulation system is a critical component of a power transformer. Its degradation may cause the device to fail while in service. If deterioration of the insulation system caused by Partial Discharge (PD) activity can be detected at an early stage, preventive maintenance measures can be taken. Due to the complex structure of power transformers, accurate locating of PD is not an easy task and is one of the main challenges in front of power utilities. Locating PD is more difficult in transformers with multiple-α windings. This problem comes to be vital in open access systems. A method for locating partial discharge within multiple-α windings is proposed, which is based on structural data of a transformer. A 66 kV/25 MVA transformer with fully interleaved winding and connected tap winding is used as test object. Wavelet transform is employed to process the partial discharge signals. Wavelet transform analysis method is a powerful tool for processing transients and non-stationary or time varying signals. Since the wavelet transform provides multi-scale analysis and time–frequency domain localization, it is particularly suitable to process the partial discharge signals. In order to improve the accuracy of the partial discharge location, a new technique for extracting Partial Discharge signals is introduced. Applying wavelet transform to a signal produces a wavelet detail coefficient distribution throughout the time-scale, which depends on the mother wavelet chosen. This technique is based on the capability of the chosen mother wavelet for generating coefficients with maximum values. The wavelet based de-noising method proposed in this paper can be successfully employed to extract PD pulse from the measured signal. It can provide enhanced information and further infer the original site of the PD pulse through capacitive ratio method. The method is described in details and the applications to determine the partial discharge location in multiple-α windings are explored.