Flying Squirrel Search Optimization Based Maximum Power Point Tracking for Photovoltaic Systems

This paper presents a Flying Squirrel Search Optimization (FSSO) based Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems operating under varying environmental and partial shading conditions. The nonlinear characteristics of PV systems and the presence of multiple local maxima under shading make conventional MPPT methods less effective. The proposed method utilizes a bio-inspired FSSO algorithm to optimize the duty cycle of a DC–DC boost converter, considering the PV output power as the objective function. The algorithm effectively balances global exploration and local exploitation, enabling accurate and fast tracking of the global maximum power point (GMPP). The performance of the proposed method is evaluated using MATLAB/Simulink under different irradiance and temperature conditions. Simulation results demonstrate that the FSSO- based MPPT technique achieves faster convergence, reduced steady-state oscillations, and improved tracking efficiency compared to conventional methods such as Perturb and Observe (P&O) and Particle Swarm Optimization (PSO). Overall, the proposed approach enhances the efficiency, stability, and reliability of photovoltaic systems, making it suitable for real-time renewable energy applications.