Narrow Band Frequency Response Analysis of Power Transformers with Deep Learning

Micah Phillip, Arvind Singh () and Craig J. Ramlal ()
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Micah Phillip: Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine 685509, Trinidad and Tobago
Arvind Singh: Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine 685509, Trinidad and Tobago
Craig J. Ramlal: Department of Electrical and Computer Engineering, The University of the West Indies, St. Augustine 685509, Trinidad and Tobago

Energies, 2023, vol. 16, issue 17, 1-14

Abstract: Frequency response analysis (FRA) is a standard technique for monitoring the integrity of the mechanical structure of power transformer windings. To date, however, there remains no suitable method for online testing using this technique. One of the main issues that persists is that any hardware designed to measure the frequencies in the range of interest would filter out frequency bands used for assessment by humans. The growth of pattern recognition capabilities in deep learning networks, however, now offers the possibility of detecting different types of faults in a narrow frequency band, which is simply not possible for human experts. This paper explores the ability of a selection of typical networks to classify common faults within different bands. The results show that networks are able to identify faults in bands where humans are unable to find them, which has implications for signal processing and electronics design in developing a system for online monitoring.

Keywords: condition monitoring; deep learning; online fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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