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New hybrid machine learning method for detecting faults in three-phase power transformers

Abdusalam, Othman, Ibrahim, Alasmer, Anayi, Fatih and Packianather, Michael 2022. New hybrid machine learning method for detecting faults in three-phase power transformers. Energies 15 (11) , e3905. 10.3390/en15113905

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Abstract

A novel hybrid machine learning technique is proposed for the protection of three-phase power transformers in this study. Here, the developed model was tested across several types of current signal fault cases from different fault conditions and examined based on a laboratory-constructed transformer system, in which internal and external faults were created. The data gathered on signals were used to develop a novel hybrid model. A process for optimal feature identification was put forward, with machine learning classifiers being trained to classify faults. The methods used included orthogonal matching pursuit and discrete wavelet transform for extraction of statistical characteristics from unprocessed data. Following this, the bees algorithm (BA) was used to create an optimized subset, minimizing the amount of data needed and making the model more accurate. In order to distinguish normal operational conditions (inrush current) from faults, an optimized feature set was used as an input for three classification algorithms: the k-nearest neighbour, support vector machine, and artificial neural network. Training was conducted via k-fold cross-validation. Comparisons were made between the proposed approach and a comparable approach, which used the genetic algorithm (GA). This model was analysed based on specificity, accuracy, precision, recall, and F1 score. The findings from the experiment suggest that the model proposed here is suitable for fault identification in a range of conditions and faults.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Publisher: MDPI
ISSN: 1996-1073
Date of First Compliant Deposit: 27 May 2022
Date of Acceptance: 23 May 2022
Last Modified: 28 May 2022 08:15
URI: https://orca.cardiff.ac.uk/id/eprint/150084

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