| Issue |
Int. J. Metrol. Qual. Eng.
Volume 10, 2019
|
|
|---|---|---|
| Article Number | 13 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/ijmqe/2019012 | |
| Published online | 11 November 2019 | |
Research Article
Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals
Electric Power Research Institute, State Grid Chongqing Electric Power Company, Chongqing 401123, PR China
* Corresponding author: longl_li@yeah.net
Received:
19
September
2019
Accepted:
17
October
2019
The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.
Key words: High voltage circuit breaker / vibration signal / feature extraction / wavelet packet energy entropy
© L. Li et al., published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.