New Hybrid Machine Learning Method for Detecting Faults in Three-Phase Power Transformers

Othman Abdusalam, Alasmer Ibrahim, Fatih Anayi and Michael Packianather
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Othman Abdusalam: Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Alasmer Ibrahim: Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Fatih Anayi: Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Michael Packianather: High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK

Energies, 2022, vol. 15, issue 11, 1-15

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.

Keywords: transformer protection; internal faults; external faults; inrush current; bees algorithm (BA); genetic algorithm (GA) (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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