Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review
Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13047 |
| _version_ | 1797382168992808960 |
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| author | Slaven Kaluđer Krešimir Fekete Kristijan Čvek Zvonimir Klaić |
| author_facet | Slaven Kaluđer Krešimir Fekete Kristijan Čvek Zvonimir Klaić |
| author_sort | Slaven Kaluđer |
| collection | DOAJ |
| description | Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method. |
| first_indexed | 2024-03-08T21:01:32Z |
| format | Article |
| id | doaj.art-5b52490b35394f2dbe65826ba78f8f64 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:32Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5b52490b35394f2dbe65826ba78f8f642023-12-22T13:50:40ZengMDPI AGApplied Sciences2076-34172023-12-0113241304710.3390/app132413047Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature ReviewSlaven Kaluđer0Krešimir Fekete1Kristijan Čvek2Zvonimir Klaić3HEP Distribution System Operator, Elektroslavonija, 31000 Osijek, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology, Department of Power Engineering, Josip Juraj Strossmayser University of Osijek, 31000 Osijek, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology, Department of Power Engineering, Josip Juraj Strossmayser University of Osijek, 31000 Osijek, CroatiaFaculty of Electrical Engineering, Computer Science and Information Technology, Department of Power Engineering, Josip Juraj Strossmayser University of Osijek, 31000 Osijek, CroatiaActive distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.https://www.mdpi.com/2076-3417/13/24/13047artificial neural networkdistribution gridislanding detectionlocal methodsdistributed generation |
| spellingShingle | Slaven Kaluđer Krešimir Fekete Kristijan Čvek Zvonimir Klaić Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review Applied Sciences artificial neural network distribution grid islanding detection local methods distributed generation |
| title | Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review |
| title_full | Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review |
| title_fullStr | Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review |
| title_full_unstemmed | Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review |
| title_short | Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review |
| title_sort | application of artificial neural networks to islanding detection in distribution grids a literature review |
| topic | artificial neural network distribution grid islanding detection local methods distributed generation |
| url | https://www.mdpi.com/2076-3417/13/24/13047 |
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