An Efficient ANN-Based MPPT Optimal Controller of a DC/DC Boost Converter for Photovoltaic Systems
| Autor: | Mohamed Salah Khireddine, Yassine Abdessemed, Mohamed Tahar Makhloufi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
General Computer Science Maximum power principle business.industry 020209 energy 020208 electrical & electronic engineering Photovoltaic system 02 engineering and technology Maximum power point tracking Power optimizer Control and Systems Engineering Control theory Boost converter 0202 electrical engineering electronic engineering information engineering Electronic engineering MATLAB business computer Electrical efficiency computer.programming_language |
| Zdroj: | Automatika. 57:109-119 |
| ISSN: | 1848-3380 0005-1144 |
| DOI: | 10.7305/automatika.2016.07.798 |
| Popis: | In this paper, a simulation study of the maximum power point tracking (MPPT) for a photovoltaic system using an artificial neural network is presented. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximizes the power output from a PV solar system for all temperature and irradiation conditions, and therefore maximizes the power efficiency. Since the maximum power point (MPP) varies, based on the PV irradiation and temperature, appropriate algorithms must be utilized to track it in order maintain the optimal operation of the system. The software Matlab/Simulink is used to develop the model of PV solar system MPPT controller. The system simulation is elaborated by combining the models established of solar PV module and a DC/DC Boost converter. The system is studied using various irradiance shading conditions. Simulation results show that the photovoltaic simulation system tracks optimally the maximum power point even under severe disturbances conditions. |
| Databáze: | OpenAIRE |
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