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MPPT technique based on neural network for photovoltaic system

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Resumo:The using of an efficient MPPT (Maximum Power Point Tracking) algorithm influences a lot in the global efficiency of the PV system. This thesis presents a detailed study based on simulation of different MPPT algorithms with their features using two systems (off-grid and on-grid). The off-grid system contains a PV array connected to a boost converter and a resistive load. On the off-grid system a simulation is presented using MATLAB/SIMULINK platform with several MPPT algorithms. The simulated MPPT algorithms are the conventionals Incremental Conductance (IncCond), Perturb and Observe (P&O), Open Circuit Voltage (OCV) and a new developed Neural Network (NN) under different environmental conditions of temperature and irradiance. As a result of the simulation, the NN algorithm has a quick response, i.e, it requires less time to reach the MPP and high efficiency and less oscillation comparing with the conventional methods. On the other hand, a single-phase two-stage photovoltaic grid-connected system is simulated which contains a PV array, a boost converter, a dc link capacitor, an inverter, an output L filter and the utility grid. In that system a control of dc link voltage, the injected current and the MPPT is made. Another MPPT algorithm based on NN (modified- NN) was also established. Showed later that is the most suitable for the system. The maximum of power is achieved when the irradiance is maximal and the temperature is minimal. Finally, a study of the influence of the variation in the climatic conditions on the output performance of the system is done.
Autores principais:Abderrahmane, Elhor
Assunto:MPPT Neural network Boost converter PV system
Ano:2021
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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