Comparison of MPPT Performance Between Firefly Algorithm and Particle Swarm Optimization for PV Systems in Partial Shading Conditions

In essence, the PV energy distributed directly to the demand is only sometimes in the optimal condition. If the irradiance or temperature received by the PV changes, this could result from a cloud obscuring the sun or other factors. Partial shading of PV can have a significant impact on its power output. Thus, the energy or power supplied to the burden varies, and even the produced energy may not be optimal. A particular control method is required for the most significant quantity of power. Maximum Power Point Tracking (MPPT) is a technique that can be utilized to optimize the PV energy output. Sadly, the practices employed to date are typically entangled in local peaks and extended periods of convergence. Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) are two heuristic control methods that can address the shortcomings of earlier techniques. This paper describes the benefits and drawbacks of PSO and FA in monitoring optimum PV power under partial shading conditions. Simulation results indicate that the FA algorithm is more reliable than the PSO algorithm in monitoring, with a success rate of 98.9 and 99.7% and a failure rate of approximately 1.3%. In this instance, FA is 1.96 percent more effective than PSO. PSO is about 0.33% quicker at monitoring.