A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation

In this paper, two new Artificial Neural Network MPPT controllers based on fixed and variable step size have been proposed and investigated. The data required to generate the ANN model are generated using the classical Perturbation and Observation algorithm. The neural network MPPT controller is developed in two steps: the offline step required for training of different neural networks parameters in order to find the optimal neural network MPPT controller (structure, activation function and training algorithm) and the Online step where the optimal neural network MPPT controller is used in PV system. The performance of the proposed variable step size and fixed step size ANN-MPPT methods are analyzed under different operating conditions using Matlab/Simulink. To validate the simulated system hardware implementation of the proposed algorithms was carried out using experimental prototype MPPT based on Flyback converter connected to Solarex MSX-60 (4 panels) and dsPIC30F4011 control circuit. Analysis and comparative study between the proposed fixed and variable step size ANN-MPPT controllers have been presented, showing a real contributions in term of tracking accuracy, response time, overshoot and steady state ripple. In addition, this paper can be considered as a review study on ANN-MPPT methods for PV systems.