Conference proceeding
Optimization of PV Model using Fuzzy-Neural Network for DC-DC converter Systems
International Renewable Energy Congress (Online), pp 1-6
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Due to the large demand for energy, energy sources, as well as the problems of the environment such as the dynamic weather conditions. Hence the world researchers nowadays are moving toward using solar energy because it gives different advantages over the traditional energy sources such as low maintenance costs, eternal sun energy, and the lack of revival of the gases of green houses. As a result, the photo-voltaic (PV) systems' power will be reduced. Under different weather conditions, maximizing the power point tracking (MPPT) is an important part to improve the solar systems power. In this paper, we introduce the neural network approaches for the PV systems. This paper also presents a novel application of Fuzzy Neural Network (FNN) in modeling a PV. The photovoltaic system model is designed with the use of MATLAB/SIMULINK software program with the connection of a DC-DC boost converter, a Maximum Power Point Tracking (MPPT) controller, a one-phase Voltage Source Converter (VSC) and a three-level bridge. The MPPT controller is used to cover the need for advanced controller that can detect the maximum power point in solar cell systems that have unstable current and voltage and keep the resultant power per cost low.
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Details
- Title
- Optimization of PV Model using Fuzzy-Neural Network for DC-DC converter Systems
- Creators
- Ahmed Ali - University of JohannesburgAli N. Hasan - University of Johannesburg
- Publication Details
- International Renewable Energy Congress (Online), pp 1-6
- Series
- International Renewable Energy Congress
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000434869100110
- Scopus ID
- 2-s2.0-85048471956
- Other Identifier
- 991022004774404721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Energy & Fuels
- Engineering, Electrical & Electronic
- Green & Sustainable Science & Technology