Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/22/7680 |
| _version_ | 1827639929608339456 |
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| author | Ahmed Sami Alhanaf Hasan Huseyin Balik Murtaza Farsadi |
| author_facet | Ahmed Sami Alhanaf Hasan Huseyin Balik Murtaza Farsadi |
| author_sort | Ahmed Sami Alhanaf |
| collection | DOAJ |
| description | Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. |
| first_indexed | 2024-03-09T16:50:55Z |
| format | Article |
| id | doaj.art-78d27ec0b09f421cb60d9d8824d33b3d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:50:55Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-78d27ec0b09f421cb60d9d8824d33b3d2023-11-24T14:40:48ZengMDPI AGEnergies1996-10732023-11-011622768010.3390/en16227680Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural NetworksAhmed Sami Alhanaf0Hasan Huseyin Balik1Murtaza Farsadi2Department of Computer Engineering, Yildiz Technical University, Istanbul 34220, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul 34295, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul 34295, TurkeyEffective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.https://www.mdpi.com/1996-1073/16/22/7680smart grid (SG)fault classification and detectiondeep neural networksANNCNN |
| spellingShingle | Ahmed Sami Alhanaf Hasan Huseyin Balik Murtaza Farsadi Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks Energies smart grid (SG) fault classification and detection deep neural networks ANN CNN |
| title | Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks |
| title_full | Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks |
| title_fullStr | Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks |
| title_full_unstemmed | Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks |
| title_short | Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks |
| title_sort | intelligent fault detection and classification schemes for smart grids based on deep neural networks |
| topic | smart grid (SG) fault classification and detection deep neural networks ANN CNN |
| url | https://www.mdpi.com/1996-1073/16/22/7680 |
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