In this paper, an optimized maximum power point tracking (MPPT) control for a standalone photovoltaic (PV) system using a three-level boost (TLB) converter is introduced. The proposed MPPT method is based on an intelligent perturb and observe algorithm using the artificial neural network (ANN-P&O) to reduce the oscillations at the maximum power point (MPP). In advance, The ANN provides the values of the voltage and the current at the MPP for any solar irradiance and cell temperature. Based on the provided voltage and current, the P&O algorithm generates the optimal duty cycle of the TLB converter to perfectly track the MPP of the PV generator for different values of cell temperature and sunlight irradiance. Besides, a proportional-integral (PI) controller is added to ensure the TLB capacitor voltage balance. The established ANN-P&O approach is validated in Matlab/Simulink and compared to the conventional P&O algorithm under various scenarios: (i) irradiance variations, (ii) temperature variations, and (iii) load variations.

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The concept introduced by MathWorks in the Simscape product is the link representation between the SIMSCAPE library components that correspond to physical connections transmitting power. In this paper, a power insulated-gate bipolar transistor (IGBT) model using MATLAB graphical software is reproduced. An electrical IGBT behavior model using the Simscape Electronics library components is developed and analyzed. This model is parameterized using the constructor datasheet to ensure a good representation of the dynamic and static IGBT behaviors. An extraction and optimization studies of the IGBT model parameters using a stochastic algorithm implemented in Matlab are presented. The proposed method is based on the Genetic Algorithm (GA) to perfectly extract and optimize the model parameters using the mathematical model circuit equations and the provided datasheet characteristics. A simulation in the Matlab/Simulink environment and a comparison with the experimental results for an IGBT device example are carried out to demonstrate the proposed model accuracy.