Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions
This paper investigates an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) technique applied to a reconfigurable photovoltaic (PV)-based battery charger. The proposed method uses training data collected from a dynamic model of the PV module to train the ANFIS...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9508417/ |
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| author | Sara A. Ibrahim Ahmed Nasr Mohamed A. Enany |
| author_facet | Sara A. Ibrahim Ahmed Nasr Mohamed A. Enany |
| author_sort | Sara A. Ibrahim |
| collection | DOAJ |
| description | This paper investigates an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) technique applied to a reconfigurable photovoltaic (PV)-based battery charger. The proposed method uses training data collected from a dynamic model of the PV module to train the ANFIS to locate the maximum power point (MPP) under different environmental conditions. Based on the estimated MPP, the proposed method can select the optimal configuration of a PV array and the corresponding global MPP under the non-uniform distribution of the temperature and irradiance. In this way, the proposed method can guarantee the highest possible power harvesting to charge a lithium-ion battery under either partial shading conditions or characteristics mismatch, achieving a high system efficiency. The proposed method is compared with the conventional MPPT scheme to verify its feasibility and effectiveness. The verification results show that the proposed method provides higher accuracy, faster response and better tracking efficiency. |
| format | Article |
| id | doaj-art-fe6c10260ae94fdc9bdb75bfb5c8e707 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe6c10260ae94fdc9bdb75bfb5c8e7072025-08-20T02:38:05ZengIEEEIEEE Access2169-35362021-01-01911445711446710.1109/ACCESS.2021.31030399508417Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating ConditionsSara A. Ibrahim0https://orcid.org/0000-0003-1804-5146Ahmed Nasr1https://orcid.org/0000-0002-7700-278XMohamed A. Enany2https://orcid.org/0000-0002-2183-3744Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, EgyptElectrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, EgyptElectrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, EgyptThis paper investigates an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) technique applied to a reconfigurable photovoltaic (PV)-based battery charger. The proposed method uses training data collected from a dynamic model of the PV module to train the ANFIS to locate the maximum power point (MPP) under different environmental conditions. Based on the estimated MPP, the proposed method can select the optimal configuration of a PV array and the corresponding global MPP under the non-uniform distribution of the temperature and irradiance. In this way, the proposed method can guarantee the highest possible power harvesting to charge a lithium-ion battery under either partial shading conditions or characteristics mismatch, achieving a high system efficiency. The proposed method is compared with the conventional MPPT scheme to verify its feasibility and effectiveness. The verification results show that the proposed method provides higher accuracy, faster response and better tracking efficiency.https://ieeexplore.ieee.org/document/9508417/Adaptive neuro-fuzzy inference system (ANFIS)battery chargingmaximum power point tracking (MPPT)non-uniform irradiancephotovoltaic system (PV)partial shading |
| spellingShingle | Sara A. Ibrahim Ahmed Nasr Mohamed A. Enany Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions IEEE Access Adaptive neuro-fuzzy inference system (ANFIS) battery charging maximum power point tracking (MPPT) non-uniform irradiance photovoltaic system (PV) partial shading |
| title | Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions |
| title_full | Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions |
| title_fullStr | Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions |
| title_full_unstemmed | Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions |
| title_short | Maximum Power Point Tracking Using ANFIS for a Reconfigurable PV-Based Battery Charger Under Non-Uniform Operating Conditions |
| title_sort | maximum power point tracking using anfis for a reconfigurable pv based battery charger under non uniform operating conditions |
| topic | Adaptive neuro-fuzzy inference system (ANFIS) battery charging maximum power point tracking (MPPT) non-uniform irradiance photovoltaic system (PV) partial shading |
| url | https://ieeexplore.ieee.org/document/9508417/ |
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