Abstract:In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power-voltage (P-V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical

The P-V curve of solar cell is nonlinear, depending on both illuminance and temperature. The MPPT technique is compulsory in order to maintain the output power of the solar cell at its maximum value. P&O MPPT technique is the most commonly used in the industry; however, the main problem is to strike a balance between tracking time and tracking accuracy. To solve the problem, variable-step size MPPT algorithms have been reported in the literatures. In this paper, three different types of variable step-size MPPT methods are implemented and compared.

No abstract