Maximum Power Point Tracking of PV Grids Using Deep Learning
In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/1123251 |
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| _version_ | 1832563519058345984 |
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| author | K. Rafeeq Ahmed Farrukh Sayeed K. Logavani T. J. Catherine Shimpy Ralhan Mahesh Singh R. Thandaiah Prabu B. Bala Subramanian Adane Kassa |
| author_facet | K. Rafeeq Ahmed Farrukh Sayeed K. Logavani T. J. Catherine Shimpy Ralhan Mahesh Singh R. Thandaiah Prabu B. Bala Subramanian Adane Kassa |
| author_sort | K. Rafeeq Ahmed |
| collection | DOAJ |
| description | In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation. |
| format | Article |
| id | doaj-art-bb12bb3c72a947b08dca8949de7f5f6d |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-bb12bb3c72a947b08dca8949de7f5f6d2025-02-03T01:19:59ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/1123251Maximum Power Point Tracking of PV Grids Using Deep LearningK. Rafeeq Ahmed0Farrukh Sayeed1K. Logavani2T. J. Catherine3Shimpy Ralhan4Mahesh Singh5R. Thandaiah Prabu6B. Bala Subramanian7Adane Kassa8Department of Electronics and Communication EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electronics and Communication EngineeringDepartment of BiotechnologyFaculty of Mechanical EngineeringIn this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.http://dx.doi.org/10.1155/2022/1123251 |
| spellingShingle | K. Rafeeq Ahmed Farrukh Sayeed K. Logavani T. J. Catherine Shimpy Ralhan Mahesh Singh R. Thandaiah Prabu B. Bala Subramanian Adane Kassa Maximum Power Point Tracking of PV Grids Using Deep Learning International Journal of Photoenergy |
| title | Maximum Power Point Tracking of PV Grids Using Deep Learning |
| title_full | Maximum Power Point Tracking of PV Grids Using Deep Learning |
| title_fullStr | Maximum Power Point Tracking of PV Grids Using Deep Learning |
| title_full_unstemmed | Maximum Power Point Tracking of PV Grids Using Deep Learning |
| title_short | Maximum Power Point Tracking of PV Grids Using Deep Learning |
| title_sort | maximum power point tracking of pv grids using deep learning |
| url | http://dx.doi.org/10.1155/2022/1123251 |
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