In several nations, photovoltaic (PV) power systems are widely employed. The results demonstrated that the SOA based MPPT method performs better in terms of tracking accuracy and efficiency. The effectiveness of the SOA based MPPT method is verified by comparing its performance with P& O and PSO (particle swarm optimization) based MPPT methods under different shading scenarios. In this paper, the SOA-based MPPT scheme is first proposed and then implemented for an 80 W PV system using the MATLAB/SIMULINK environment. When compared to other evolutionary techniques, it uses fewer operators and modification parameters, which is advantageous when considering the rapid design process. The SOA is a new member of the bio-inspired algorithms. To address this issue, this paper introduces a new MPPT method based on the Seagull Optimization Algorithm (SOA) to operate PV systems at GMPP with high efficiency. This results in considerable energy loss. In this scenerio, conventional MPPT methods, including pertub and observe (P&O) and incremental conductance (INC), fail to differentiate between a GMPP and a LMPP, as they converge on the MPP that makes contact first, which in most cases is one of the LMPPs. However, under partial shading conditions, the PV cells/panels do not receive uniform insolation due to several power maxima appear on the PV array's P–V characteristic, a global MPP (GMPP) and two or more local MPPs (LMPPs). The use of a maximum power point (MPP) tracking (MPPT) controller is required for photovoltaic (PV) systems to extract maximum power from PV panels.
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