Online stability assessment for isolated microgrid via LASSO based neural network algorithm
Online prediction of the dominant modes is very important for microgrid operation. The dominant modes determine microgrid stability and the active and reactive power oscillations. Therefore, online prediction of these modes is essential to check the microgrid stability periodically. Consequently, th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174524003271 |
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| author | Ahmed Lasheen Hatem F. Sindi Hatem H. Zeineldin Mohammed Y. Morgan |
| author_facet | Ahmed Lasheen Hatem F. Sindi Hatem H. Zeineldin Mohammed Y. Morgan |
| author_sort | Ahmed Lasheen |
| collection | DOAJ |
| description | Online prediction of the dominant modes is very important for microgrid operation. The dominant modes determine microgrid stability and the active and reactive power oscillations. Therefore, online prediction of these modes is essential to check the microgrid stability periodically. Consequently, this paper introduces an artificial intelligent algorithm to identify the dominant modes of the microgrid. This algorithm combines a cascaded feedforward neural network with the least absolute shrinkage and select operator (LASSO). The LASSO algorithm is used to extract the most important data that affects the dominant modes. On the other hand, the cascaded feedforward neural network is trained using LASSO data to identify the microgrid dominant modes. The proposed algorithm is tested using a 6-bus AC microgrid. The results show that the proposed algorithm significantly determines the dominant modes of the microgrid by using a minimum set of data determined by LASSO. |
| format | Article |
| id | doaj-art-f69e835bb1c94197a9c0bc2f89ad96b6 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-f69e835bb1c94197a9c0bc2f89ad96b62024-12-30T04:15:56ZengElsevierEnergy Conversion and Management: X2590-17452025-01-0125100849Online stability assessment for isolated microgrid via LASSO based neural network algorithmAhmed Lasheen0Hatem F. Sindi1Hatem H. Zeineldin2Mohammed Y. Morgan3Electrical Power Engineering Dept., Faculty of Engineering, Cairo University, Egypt; Corresponding author.Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Electrical and Computer Engineering Dept., King Abdulaziz University, Jeddah, Saudi ArabiaElectrical Power Engineering Dept., Faculty of Engineering, Cairo University, Egypt; Electrical Engineering, Advanced Power and Energy Center, Khalifa University, Abu Dhabi, the United Arab EmiratesElectrical Power Engineering Dept., Faculty of Engineering, Cairo University, EgyptOnline prediction of the dominant modes is very important for microgrid operation. The dominant modes determine microgrid stability and the active and reactive power oscillations. Therefore, online prediction of these modes is essential to check the microgrid stability periodically. Consequently, this paper introduces an artificial intelligent algorithm to identify the dominant modes of the microgrid. This algorithm combines a cascaded feedforward neural network with the least absolute shrinkage and select operator (LASSO). The LASSO algorithm is used to extract the most important data that affects the dominant modes. On the other hand, the cascaded feedforward neural network is trained using LASSO data to identify the microgrid dominant modes. The proposed algorithm is tested using a 6-bus AC microgrid. The results show that the proposed algorithm significantly determines the dominant modes of the microgrid by using a minimum set of data determined by LASSO.http://www.sciencedirect.com/science/article/pii/S2590174524003271LASSOCascaded neural networkMicrogrid identification |
| spellingShingle | Ahmed Lasheen Hatem F. Sindi Hatem H. Zeineldin Mohammed Y. Morgan Online stability assessment for isolated microgrid via LASSO based neural network algorithm Energy Conversion and Management: X LASSO Cascaded neural network Microgrid identification |
| title | Online stability assessment for isolated microgrid via LASSO based neural network algorithm |
| title_full | Online stability assessment for isolated microgrid via LASSO based neural network algorithm |
| title_fullStr | Online stability assessment for isolated microgrid via LASSO based neural network algorithm |
| title_full_unstemmed | Online stability assessment for isolated microgrid via LASSO based neural network algorithm |
| title_short | Online stability assessment for isolated microgrid via LASSO based neural network algorithm |
| title_sort | online stability assessment for isolated microgrid via lasso based neural network algorithm |
| topic | LASSO Cascaded neural network Microgrid identification |
| url | http://www.sciencedirect.com/science/article/pii/S2590174524003271 |
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