A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers
Integration of a significant number of domestic electrical vehicle (EV) charging stations into the power distribution infrastructure can give rise to several protection problems. Therefore, we propose a new method to detect short-circuit faults in distribution networks with high penetration of resid...
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| Format: | Article |
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124004248 |
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| author | Seyed Amir Hosseini Behrooz Taheri Seyed Hossein Hesamedin Sadeghi Adel Nasiri |
| author_facet | Seyed Amir Hosseini Behrooz Taheri Seyed Hossein Hesamedin Sadeghi Adel Nasiri |
| author_sort | Seyed Amir Hosseini |
| collection | DOAJ |
| description | Integration of a significant number of domestic electrical vehicle (EV) charging stations into the power distribution infrastructure can give rise to several protection problems. Therefore, we propose a new method to detect short-circuit faults in distribution networks with high penetration of residential EV chargers. In this method, first, the features of voltage and current waveforms in various operational scenarios are extracted through a two-dimensional modeling. These features are then used to train a deep learning model based on black widow optimization bi-directional long short-term memory (BWO-BiLSTM) technique. In contrast with the conventional adaptive protection schemes, the proposed method can perform accurately in the presence of fast and unpredictable network topology, without requiring to determine a large number of threshold values to detect a fault, or relying on communication links. The effectiveness of the proposed method is investigated through a series of case studies on a modified IEEE 69-bus distribution network with a substantial penetration of residential EV chargers. The results show the proposed method's ability to detect all types of faults within 5 ms. Since it employs a machine learning algorithm for fault detection, the method's accuracy is 98.5 %, surpassing the accuracy of k-nearest neighbors (KNN) and conventional LSTM models. Additionally, the results confirm its optimal performance under noisy conditions. Even with noise in the sampled signals at a level of 10 dB, the method's accuracy remains higher than that of other methods, with a value of 96.9 %. |
| format | Article |
| id | doaj-art-b70cca2ed9ab4718bac67352ed0bc262 |
| institution | OA Journals |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-b70cca2ed9ab4718bac67352ed0bc2622025-08-20T01:56:41ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-011010084510.1016/j.prime.2024.100845A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargersSeyed Amir Hosseini0Behrooz Taheri1Seyed Hossein Hesamedin Sadeghi2Adel Nasiri3Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, IranDepartment of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran; Corresponding author.Department of Electrical Engineering and Computer Science, University of South Carolina, Columbia, SC, USAIntegration of a significant number of domestic electrical vehicle (EV) charging stations into the power distribution infrastructure can give rise to several protection problems. Therefore, we propose a new method to detect short-circuit faults in distribution networks with high penetration of residential EV chargers. In this method, first, the features of voltage and current waveforms in various operational scenarios are extracted through a two-dimensional modeling. These features are then used to train a deep learning model based on black widow optimization bi-directional long short-term memory (BWO-BiLSTM) technique. In contrast with the conventional adaptive protection schemes, the proposed method can perform accurately in the presence of fast and unpredictable network topology, without requiring to determine a large number of threshold values to detect a fault, or relying on communication links. The effectiveness of the proposed method is investigated through a series of case studies on a modified IEEE 69-bus distribution network with a substantial penetration of residential EV chargers. The results show the proposed method's ability to detect all types of faults within 5 ms. Since it employs a machine learning algorithm for fault detection, the method's accuracy is 98.5 %, surpassing the accuracy of k-nearest neighbors (KNN) and conventional LSTM models. Additionally, the results confirm its optimal performance under noisy conditions. Even with noise in the sampled signals at a level of 10 dB, the method's accuracy remains higher than that of other methods, with a value of 96.9 %.http://www.sciencedirect.com/science/article/pii/S2772671124004248Electrical vehicleDistribution networkShort circuit faultsProtectionDeep learning model |
| spellingShingle | Seyed Amir Hosseini Behrooz Taheri Seyed Hossein Hesamedin Sadeghi Adel Nasiri A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers e-Prime: Advances in Electrical Engineering, Electronics and Energy Electrical vehicle Distribution network Short circuit faults Protection Deep learning model |
| title | A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| title_full | A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| title_fullStr | A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| title_full_unstemmed | A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| title_short | A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| title_sort | deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers |
| topic | Electrical vehicle Distribution network Short circuit faults Protection Deep learning model |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124004248 |
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