In single photovoltaic (PV) systems, inverters are highly important for transforming DC voltage into AC voltage with predetermined amplitude and frequency control. A good inverter must provide a stable output voltage and low harmonic distortion during potential load variation, and it must recover its stability in an efficient manner while maintaining the quality of power during disturbances. Fixed controllers usually do not provide satisfactory performance because of the inherent nonlinear characteristics of the systems, and this drives the adoption of intelligent control techniques. One area of interest is fuzzy logic controllers (FLCs), which can provide a good control measure of nonlinear dynamics and do not require use of a specific mathematical model. This study introduces the Firefly Algorithm to optimize fuzzy controller membership functions for improved voltage regulation, reduced MSE, and lower THD. The input and output membership functions are optimized in order to minimize the mean square error (MSE) of the output voltage. The proposed controller is tested for different load conditions including resistive loads, inductive loads, and non-linear loads; the performance is compared to a traditional fuzzy logic controller (FLC) as well as FLCs optimized using a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results from the simulation indicate the performance achieved from Firefly Algorithm (FA) is acceptable when compared with voltage regulation, harmonic distortion (THD), and dynamic responses for stand-alone photovoltaic systems. Simulation results indicate that the FA-optimized fuzzy logic controller produces a minimum total harmonic distortion of 2.89% and a mean square error of 0.0071, thus showing its superiority over the traditional PI and fuzzy logic controllers for various load conditions.
Nouri et al. (Mon,) studied this question.