A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals
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Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland
2
Faculty of Electrical Engineering Automatic Control and Informatics, Institute of Automatic Control, Opole University of Technology, 45-758 Opole, Poland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5212; https://doi.org/10.3390/s19235212
Received: 17 October 2019
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Revised: 18 November 2019
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Accepted: 25 November 2019
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Published: 28 November 2019
(This article belongs to the Special Issue Acoustic, UHF and RF Sensor Technology for Partial Discharge Detection)
Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers.
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MDPI and ACS Style
Kunicki, M.; Wotzka, D. A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals. Sensors 2019, 19, 5212. https://doi.org/10.3390/s19235212
AMA Style
Kunicki M, Wotzka D. A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals. Sensors. 2019; 19(23):5212. https://doi.org/10.3390/s19235212
Chicago/Turabian StyleKunicki, Michał, and Daria Wotzka. 2019. "A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals" Sensors 19, no. 23: 5212. https://doi.org/10.3390/s19235212
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