Artificial neural network based maximum power point tracking controller for photovoltaic standalone system
This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum powe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43074/ http://psasir.upm.edu.my/id/eprint/43074/1/Artificial%20neural%20network%20based%20maximum%20power%20point%20tracking%20controller%20for%20photovoltaic%20standalone%20system.pdf |
| _version_ | 1848850130904547328 |
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| author | Khanaki, Razieh Mohd Radzi, Mohd Amran Marhaban, Mohammad Hamiruce |
| author_facet | Khanaki, Razieh Mohd Radzi, Mohd Amran Marhaban, Mohammad Hamiruce |
| author_sort | Khanaki, Razieh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well. |
| first_indexed | 2025-11-15T10:01:24Z |
| format | Article |
| id | upm-43074 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:01:24Z |
| publishDate | 2016 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-430742016-05-18T01:24:44Z http://psasir.upm.edu.my/id/eprint/43074/ Artificial neural network based maximum power point tracking controller for photovoltaic standalone system Khanaki, Razieh Mohd Radzi, Mohd Amran Marhaban, Mohammad Hamiruce This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well. Taylor & Francis 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43074/1/Artificial%20neural%20network%20based%20maximum%20power%20point%20tracking%20controller%20for%20photovoltaic%20standalone%20system.pdf Khanaki, Razieh and Mohd Radzi, Mohd Amran and Marhaban, Mohammad Hamiruce (2016) Artificial neural network based maximum power point tracking controller for photovoltaic standalone system. International Journal of Green Energy, 13 (3). pp. 283-291. ISSN 1543-5075; ESSN: 1543-5083 http://www.tandfonline.com/doi/abs/10.1080/15435075.2014.910783?journalCode=ljge20 10.1080/15435075.2014.910783 |
| spellingShingle | Khanaki, Razieh Mohd Radzi, Mohd Amran Marhaban, Mohammad Hamiruce Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title | Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title_full | Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title_fullStr | Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title_full_unstemmed | Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title_short | Artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| title_sort | artificial neural network based maximum power point tracking controller for photovoltaic standalone system |
| url | http://psasir.upm.edu.my/id/eprint/43074/ http://psasir.upm.edu.my/id/eprint/43074/ http://psasir.upm.edu.my/id/eprint/43074/ http://psasir.upm.edu.my/id/eprint/43074/1/Artificial%20neural%20network%20based%20maximum%20power%20point%20tracking%20controller%20for%20photovoltaic%20standalone%20system.pdf |