Domingo, D.; Kareem, A.B.; Okwuosa, C.N.; Custodio, P.M.; Hur, J.-W. Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection. Electronics2024, 13, 926.
Domingo, D.; Kareem, A.B.; Okwuosa, C.N.; Custodio, P.M.; Hur, J.-W. Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection. Electronics 2024, 13, 926.
Domingo, D.; Kareem, A.B.; Okwuosa, C.N.; Custodio, P.M.; Hur, J.-W. Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection. Electronics2024, 13, 926.
Domingo, D.; Kareem, A.B.; Okwuosa, C.N.; Custodio, P.M.; Hur, J.-W. Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection. Electronics 2024, 13, 926.
Abstract
To enhance the overall reliability of the power system, engineers have redirected their focus toward health monitoring and early detection of faults in transformers. Among these faults, transformer core defects demand particular attention. While fault simulation using software has traditionally been the preferred approach, these methods suffer from data inaccuracies in real-world conditions. Consequently, conducting actual experimental setups with induced faults is imperative to investigate core issues. This study uses Hilbert Transform (HT) as a signal processing technique to extract crucial data characteristics, thereby enhancing the performance of the classifier model. The research involves analyzing electric current signals from a single-phase 1kVA transformer. A comparative assessment of our proposed model was conducted using raw data and Fast Fourier Transform (FFT), evaluating accuracy, precision, recall, F1-score, and computational time. The results demonstrate a significant improvement in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, which exhibited values of 83.89%, 84.39%, 83.89%, 83.79%, and 0.0156 seconds, respectively. Future work aims to extend this analysis under different loading conditions.
Copyright:
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.
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