MPPT Efficiency Test by Neural Networks and P&O Algorithm


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Abstract


In this paper we purpose to compare two methods of maximum power point tracking (MPPT). This comparison relates the photovoltaic conversion efficiency, response time and system performance. These two techniques are, on the one hand, the artificial neural network model based on the architecture Multi-Layer Perceptron (MLP) whose training is based on experimental data and on the other hand, the "Perturb and Observe" algorithm (P & O) using the perturbation of the duty cycle ratio. Both methods are used to specify the duty cycle was applied to DC-DC boost converter corresponding to the MPPT of solar photovoltaic generator, under different atmospheric conditions. The performance of both approaches is validated by experimental data confronted to simulations obtained using the MATLAB / Simulink software.
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Keywords


Photovoltaic Power Generation; Boost Converter; Artificial Neural Network; MPPT Algorithms; Perturb And Observe; Matlab/Simulink

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References


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