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Uncertain conditions involving partial shading can be found in large-scale solar photovoltaic (PV) systems. There is a possibility that the performance of the PV system will suffer as a result of partial shading conditions (PSCs) because it creates multiple peaks in the power&#x2013;voltage (P&#x2013;V) characteristics. Nevertheless, for the photovoltaic system to be utilized in the most effective manner, it needs to be run at a global maximum power point (GMPP). A new strategy based on the falcon optimization algorithm (FOA) is introduced in this paper for the monitoring of GMPP. The perturb and observe (P&#x0026;O) and the Particle swarm optimization (PSO) techniques have certain drawbacks that can be resolved using a new optimization method known as the FOA. These limitations include a lower convergence speed and steady-state oscillations. The tracking performance of the proposed method is evaluated and compared to that of three MPPT algorithms, namely grey wolf optimization (GWO), PSO and P&#x0026;O, for a PV array that is functioning under PSCs and displaying numerous peaks. An implementation of the proposed FOA-MPPT algorithm on a PV system was carried out with the help of MATLAB/SIMULINK. Simulation tests conducted under a variety of partial shading patterns reveal that the proposed FOA outperforms all three MPPT algorithms: GWO, PSO, and P&#x0026;O. Simulation results show that the MPPT efficiency of FOA in four different partial shading conditions is 99.93 %, 99.82 %, 99.80 %, and 99.81 %, Furthermore, the simulation results show that the tracking time of proposed FOA in four different partial shading conditions is 0.4 s, 0.41 s, 0.39 s, and 0.41 s, respectively. Moreover, the proposed FOA is tested using actual and measurable data from Neom, Saudi Arabia. According to the simulation results, the proposed FOA generates significantly more revenue than other compared algorithms.

An Effective Falcon Optimization Algorithm Based MPPT Under Partial Shaded Photovoltaic Systems