Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM
A modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when d...
| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
IEEE
2024-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/10508728/ |
| _version_ | 1827260590368751616 |
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| author | Ahmed Sami Alhanaf Murtaza Farsadi Hasan Huseyin Balik |
| author_facet | Ahmed Sami Alhanaf Murtaza Farsadi Hasan Huseyin Balik |
| author_sort | Ahmed Sami Alhanaf |
| collection | DOAJ |
| description | A modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when dynamic fault currents from renewable energy sources are present. To address these challenges, three unique deep learning models that make use of Deep Neural Networks (DNN) have been proposed. CNN, LSTM, and Hybrid CNN-LSTM are deep learning models. Line faulty identification (LF), fault classification (FC), and fault location estimate (FL) are the subjects on which they concentrate. These models analyze data gathered both pre and post faults occur in order to enhance decision making. Signals including the voltage and current were fed into these models from many different locations across the test networks. Once the 1D CNN has extracted characteristics from the gathered signals, LSTM uses these features to make accurate estimations and identify faults. Complex data are compatible with this method in terms of optimal outcomes. Using training and testing data from transmission line failure simulations, the proposed approaches were evaluated on the IEEE 6-bus and IEEE 9-bus systems. The tests encompassed a range of fault classes, locations, and ground fault resistances at various locations. Distributed Generator (DG) resources were additionally included in the system architecture and changes in the topology of the networks were considered in terms of location and number of DG resources. The results demonstrated that the proposed algorithms outperformed contemporary technologies in terms of detection, classification, and location accuracy. They demonstrated high accuracy and robustness in their performance. |
| first_indexed | 2025-03-22T02:44:54Z |
| format | Article |
| id | doaj.art-e74b51d73e7b4b8e80ea94ca940f09bd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-03-22T02:44:54Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e74b51d73e7b4b8e80ea94ca940f09bd2024-05-02T23:01:22ZengIEEEIEEE Access2169-35362024-01-0112599535997510.1109/ACCESS.2024.339416610508728Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTMAhmed Sami Alhanaf0https://orcid.org/0000-0002-0641-4234Murtaza Farsadi1https://orcid.org/0000-0001-6090-3824Hasan Huseyin Balik2Department of Computer Engineering, Yildiz Technical University (YTU), Istanbul, TurkeyDepartment of Electrical Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, TurkeyA modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when dynamic fault currents from renewable energy sources are present. To address these challenges, three unique deep learning models that make use of Deep Neural Networks (DNN) have been proposed. CNN, LSTM, and Hybrid CNN-LSTM are deep learning models. Line faulty identification (LF), fault classification (FC), and fault location estimate (FL) are the subjects on which they concentrate. These models analyze data gathered both pre and post faults occur in order to enhance decision making. Signals including the voltage and current were fed into these models from many different locations across the test networks. Once the 1D CNN has extracted characteristics from the gathered signals, LSTM uses these features to make accurate estimations and identify faults. Complex data are compatible with this method in terms of optimal outcomes. Using training and testing data from transmission line failure simulations, the proposed approaches were evaluated on the IEEE 6-bus and IEEE 9-bus systems. The tests encompassed a range of fault classes, locations, and ground fault resistances at various locations. Distributed Generator (DG) resources were additionally included in the system architecture and changes in the topology of the networks were considered in terms of location and number of DG resources. The results demonstrated that the proposed algorithms outperformed contemporary technologies in terms of detection, classification, and location accuracy. They demonstrated high accuracy and robustness in their performance.https://ieeexplore.ieee.org/document/10508728/Deep learningsmart gridsfault detectionfault classification and locationCNNLSTM |
| spellingShingle | Ahmed Sami Alhanaf Murtaza Farsadi Hasan Huseyin Balik Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM IEEE Access Deep learning smart grids fault detection fault classification and location CNN LSTM |
| title | Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM |
| title_full | Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM |
| title_fullStr | Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM |
| title_full_unstemmed | Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM |
| title_short | Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM |
| title_sort | fault detection and classification in ring power system with dg penetration using hybrid cnn lstm |
| topic | Deep learning smart grids fault detection fault classification and location CNN LSTM |
| url | https://ieeexplore.ieee.org/document/10508728/ |
| work_keys_str_mv | AT ahmedsamialhanaf faultdetectionandclassificationinringpowersystemwithdgpenetrationusinghybridcnnlstm AT murtazafarsadi faultdetectionandclassificationinringpowersystemwithdgpenetrationusinghybridcnnlstm AT hasanhuseyinbalik faultdetectionandclassificationinringpowersystemwithdgpenetrationusinghybridcnnlstm |