Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks
The widespread integration of renewable energy sources to the main electrical grids has led to the increased adoption of AC microgrids. However, the protection of AC microgrids is a challenging task due to inverter interfaces, bidirectional power flow, multiple modes of operation and the requirement...
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10583937/ |
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| author | J. A. R. R. Jayasinghe J. H. E. Malindi R. M. A. M. Rajapaksha V. Logeeshan Chathura Wanigasekara |
| author_facet | J. A. R. R. Jayasinghe J. H. E. Malindi R. M. A. M. Rajapaksha V. Logeeshan Chathura Wanigasekara |
| author_sort | J. A. R. R. Jayasinghe |
| collection | DOAJ |
| description | The widespread integration of renewable energy sources to the main electrical grids has led to the increased adoption of AC microgrids. However, the protection of AC microgrids is a challenging task due to inverter interfaces, bidirectional power flow, multiple modes of operation and the requirement for selective phase tripping. This paper presents an innovative artificial neural network (ANN) based approach for fast and accurate identification and localization of symmetrical and asymmetrical faults occurring in the distribution networks of AC microgrids. In the proposed methodology, the three phase and the neutral currents which are sampled at either ends of the distribution lines, undergo discrete wavelet transform to extract the features exhibited during faults in the network. These features are used by two neural networks for classification and localization of the fault. To achieve high accuracy and computational efficiency, the network architectures of the ANNs are optimized, and the extracted features contain the detailed information required for ANNs to clearly distinguish different fault types and locations. A comprehensive evaluation and validation reveal that the proposed scheme accurately and efficiently classifies and localizes faults in AC microgrids. The existing research gap of fault localization in AC microgrids is also addressed through this approach. |
| format | Article |
| id | doaj-art-960ee4011aad4d7e941d3ddc9f6cd5a1 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-960ee4011aad4d7e941d3ddc9f6cd5a12025-01-21T00:03:00ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011130331310.1109/OAJPE.2024.342238710583937Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural NetworksJ. A. R. R. Jayasinghe0https://orcid.org/0009-0008-5992-4968J. H. E. Malindi1https://orcid.org/0009-0001-7826-5418R. M. A. M. Rajapaksha2V. Logeeshan3https://orcid.org/0000-0003-3767-8280Chathura Wanigasekara4https://orcid.org/0000-0003-4371-6108Department of Electrical Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Electrical Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Electrical Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Electrical Engineering, University of Moratuwa, Moratuwa, Sri LankaInstitute for the Protection of Maritime Infrastructures, German Aerospace Centre (DLR), Bremerhaven, GermanyThe widespread integration of renewable energy sources to the main electrical grids has led to the increased adoption of AC microgrids. However, the protection of AC microgrids is a challenging task due to inverter interfaces, bidirectional power flow, multiple modes of operation and the requirement for selective phase tripping. This paper presents an innovative artificial neural network (ANN) based approach for fast and accurate identification and localization of symmetrical and asymmetrical faults occurring in the distribution networks of AC microgrids. In the proposed methodology, the three phase and the neutral currents which are sampled at either ends of the distribution lines, undergo discrete wavelet transform to extract the features exhibited during faults in the network. These features are used by two neural networks for classification and localization of the fault. To achieve high accuracy and computational efficiency, the network architectures of the ANNs are optimized, and the extracted features contain the detailed information required for ANNs to clearly distinguish different fault types and locations. A comprehensive evaluation and validation reveal that the proposed scheme accurately and efficiently classifies and localizes faults in AC microgrids. The existing research gap of fault localization in AC microgrids is also addressed through this approach.https://ieeexplore.ieee.org/document/10583937/AC microgridsartificial neural network (ANN)discrete wavelet transform (DWT)fault classificationfault localization |
| spellingShingle | J. A. R. R. Jayasinghe J. H. E. Malindi R. M. A. M. Rajapaksha V. Logeeshan Chathura Wanigasekara Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks IEEE Open Access Journal of Power and Energy AC microgrids artificial neural network (ANN) discrete wavelet transform (DWT) fault classification fault localization |
| title | Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks |
| title_full | Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks |
| title_fullStr | Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks |
| title_full_unstemmed | Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks |
| title_short | Classification and Localization of Faults in AC Microgrids Through Discrete Wavelet Transform and Artificial Neural Networks |
| title_sort | classification and localization of faults in ac microgrids through discrete wavelet transform and artificial neural networks |
| topic | AC microgrids artificial neural network (ANN) discrete wavelet transform (DWT) fault classification fault localization |
| url | https://ieeexplore.ieee.org/document/10583937/ |
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