New improved MPPT based on artificial neural network and PI controller for photovoltaic applications

Yassine El Aidi Idrissi, Khalid Assalaou, Lahoussine Elmahni, Elmostafa Aitiaz


This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controller, a proportional integral (PI) controller is also included. In addition, the performance of an ANN-based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.


ANN-PI; Boost converter; MPPT; P&O; Photovoltaic application

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