Detection and wavelet analysis of partial discharges using an optical fibre interferometric sensor for high-power transformers

被引:16
作者
Lamela-Rivera, H [1 ]
Macià-Sanahuja, C [1 ]
García-Souto, JA [1 ]
机构
[1] Univ Carlos III Madrid, Optoelect & Laser Technol Grp, Dept Elect, Madrid 28911, Spain
来源
JOURNAL OF OPTICS A-PURE AND APPLIED OPTICS | 2003年 / 5卷 / 01期
关键词
interferometry; optical fibre sensors; acoustic waves measurements; partial discharges; wavelet analysis; power transformers;
D O I
10.1088/1464-4258/5/1/310
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The main problem in power transformers is the degradation of the isolation, and partial discharges are a major contribution to this shortcoming. Partial discharges inside medium- and high-power transformers generate ultrasonic pressure waves which are usually detected by using acoustic piezoelectric sensors placed outside the transformers. In this paper we describe an optical fibre interferometric sensor we are developing for the detection and location of partial discharges by measuring inside the power transformers. This non-intrusive optical fibre sensor allows a high level of sensitivity to be achieved for the typical small pressure variations created by partial discharges, specially if compared with the results obtained by acoustic piezoelectric external sensors. We present interferometrically detected levels of partial discharge. These results indicate that the developed optical fibre sensor is able to detect a partial discharge in the range of 10(3) pC that produces an optical phase shift of about 2 rad with a resolution of 30 pC in charge means. Finally, the wavelet analysis is used to identify the acoustic patterns instead of the more commonly used Fourier analysis. When transforming the measured signal to the frequency domain, the temporal information disappears. But the acoustic signals really contain numerous transitory pieces of information to be considered. By looking at a Fourier-transformed signal it is impossible to state when a particular event took place. The wavelet analysis helps to solve this problem, and some results are presented here to show this.
引用
收藏
页码:66 / 72
页数:7
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