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Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

  • Published:2015-11 Issue:8 Volume:9 Page:1043-1063
  • ISSN:1752-1416
  • Container-title:IET Renewable Power Generation
  • language:en
  • Short-container-title:IET Renewable Power Generation

Author:

Elobaid Lina M.1,Abdelsalam Ahmed K.1,Zakzouk Ezeldin E.1

Affiliation:

1. Electrical Engineering DepartmentCollege of Engineering and TechnologyArab Academy for Science and Technology (AAST)AlexandriaEgypt

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

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1. An IoT-enabled device for remotely monitoring and controlling solar photovoltaic systems;ICST Transactions on Scalable Information Systems;2024-09-04

2. Super-twisting MPPT control for grid-connected PV/battery system using higher order sliding mode observer;Scientific Reports;2024-07-18

3. Enhancing Power Generation in Photovoltaic Systems: A Comparison of AI Techniques;2024 IEEE Workshop on Control and Modeling for Power Electronics (COMPEL);2024-06-24

4. Optimizing Photovoltaic System Efficiency Through a Kalman Filter Driven Approach for MPPT in Partial Shading Conditions;2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET);2024-05-16

5. RETRACTED: Self fuzzy-genetic approach for boosting photovoltaic efficiency;Journal of Intelligent & Fuzzy Systems;2024-04-02
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