Time Reversal vs. Integration of Time Reversal with Convolution Neural Network in Diagnosing Partial Discharge in Power Transformer

Permit Mathuhu Sekatane () and Pitshou Bokoro
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Permit Mathuhu Sekatane: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa
Pitshou Bokoro: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa

Energies, 2023, vol. 16, issue 23, 1-18

Abstract: Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of time. The paper investigates the use of time reversal and the integration of time reversal with convolution neural networks (CNNs) for diagnosing PD in power transformers. We compare the performance of these techniques on a dataset of PD signals collected from power transformers. We propose a novel method of using time reversal as a pre-processing step to improve the accuracy of CNNs on noisy or distorted signals. Our experimental results demonstrate that this approach can significantly enhance the performance of CNNs on various datasets, including speech, audio, and image datasets. This paper provides a novel approach to signal processing and demonstrates the potential of time reversal as a pre-processing step in CNNs.

Keywords: machine learning; time reversal; convolution neural networks; acoustic signals (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
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