An Experimental Testing of Optimized Fuzzy Logic‐Based MPPT for a Standalone PV System Using Genetic Algorithms

Abstract

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)-based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions.

Modeling and Performance Analysis of a Grid-Connected Photovoltaic System with Advanced Controller considering Varying Environmental Conditions

“…When the difference between the instant power PðtÞ and the real power P ðt − 1Þ is positive (dP/dV > 0), the direction of an operational point is located toward the MPP by increasing the reference voltage in the opposite direction. A PV voltage and current must be perturbed in both directions to find the MPP [13]. As a result, the temporal complexity of this algorithm is lowered; but, as it approaches the MPP, it continues to perturb in both directions rather than stopping there.…”

“…In recent research, the traditional FLC-MPPT problem has been solved by using MPPT design with the ANN approach. It generates a heuristic output function based on digital data, and a comprehensive understanding of the PV parameters is not necessary when designing the MPPT [13]. However, appropriate training is required to develop an ANN-based MPPT.…”