Conference proceeding
Commensurate Evaluation of Support Vector Machine and Recurrent Neural Network MPPT Algorithm for a PV system under different weather conditions
2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019), pp 329-335
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The expeditious broadening of Photovoltaic (PV) energy has attracted the private and government precinct world-wide due to the reduction of costs and being cleaner source of energy. However, most of the maximum power point tracking (MPPT) controller are inefficient under rapid change of environmental conditions. Under partial shading conditions (PSC) MPPT controllers fail to track global maximum power point (GMMP). Therefore, it is essential to propose MPPT controller that will he able to locate GMPP. In this study, the two powerful machine learning and deep learning MPPT algorithms are used to force the PV system to operate at higher efficiency under sudden change in solar irradiance and temperature. Support Vector Machine (SVM) and Recurrent Neural Network (RNN) performances are validated and proved using MATLAB SIMULINK simulation software.
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Details
- Title
- Commensurate Evaluation of Support Vector Machine and Recurrent Neural Network MPPT Algorithm for a PV system under different weather conditions
- Creators
- Mpho Sam Nkambule - University of JohannesburgAli N. Hasan - University of JohannesburgAhmed Ali - University of Johannesburg
- Publication Details
- 2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019), pp 329-335
- Publisher
- IEEE
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000552654100060
- Scopus ID
- 2-s2.0-85080877334
- Other Identifier
- 991022004772204721
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- Web of Science research areas
- Engineering, Electrical & Electronic