Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System
This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2012/798361 |
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| _version_ | 1832552060308946944 |
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| author | Subiyanto Azah Mohamed Hussain Shareef |
| author_facet | Subiyanto Azah Mohamed Hussain Shareef |
| author_sort | Subiyanto |
| collection | DOAJ |
| description | This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error. |
| format | Article |
| id | doaj-art-ff7e96435d8c428cb8bd162a3f93fe60 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-ff7e96435d8c428cb8bd162a3f93fe602025-02-03T05:59:48ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2012-01-01201210.1155/2012/798361798361Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic SystemSubiyanto0Azah Mohamed1Hussain Shareef2Department of Electrical, Electronic, and Systems Engineering, National University of Malaysia, Bangi, 43600 Selangor, MalaysiaDepartment of Electrical, Electronic, and Systems Engineering, National University of Malaysia, Bangi, 43600 Selangor, MalaysiaDepartment of Electrical, Electronic, and Systems Engineering, National University of Malaysia, Bangi, 43600 Selangor, MalaysiaThis paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error.http://dx.doi.org/10.1155/2012/798361 |
| spellingShingle | Subiyanto Azah Mohamed Hussain Shareef Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System International Journal of Photoenergy |
| title | Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System |
| title_full | Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System |
| title_fullStr | Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System |
| title_full_unstemmed | Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System |
| title_short | Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System |
| title_sort | hopfield neural network optimized fuzzy logic controller for maximum power point tracking in a photovoltaic system |
| url | http://dx.doi.org/10.1155/2012/798361 |
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