Design of a MPPT System Based on Modified Grey Wolf Optimization Algorithm in Photovoltaic System under Partially Shaded Condition
Keywords:Grey wolf optimization (GWO), Maximum Power Point Tracking (MPPT), Partial shading conditions (PSCs), Photo-voltaic (PV)
Conventional Maximum Potential Monitoring strategies such as perturbation and observation, incremental conduct, and climbing can effectively monitor the maximum power point in uniform shading, whereas failing in a partially shaded condition. Nevertheless, it is difficult to achieve optimal and reliable power by using photovoltaics. So, to solve this issue, this article proposes to monitor the photovoltaic system's global optimum powerpoint for partial shading with a Modified Gray Wolf Optimizer (MGWO) based maximum power point tracking algorithm. Under partial shadows, a mathematical model of the PV system is built with a single diode, EGWO is used to monitor global maximum power points. A photovoltaic system includes deciding which converter is used to increase photovoltaic power generation. The MPPT architecture uses a modified gray wolf optimization algorithm to quickly track the output power and reduce photovoltaic oscillations. The efficiency of the maximum power tracker is better than the GWO algorithm of up to 0,4 s with the modified gray wolf optimization algorithm. Converters are used to resolve the power losses often occurring in PV systems with a soft-buck converter process. The output of the power generator is greater than the soft-switching buck converter. The simulation and experimental results obtained suggest that both the P & O and IPSO MPPTs are superior to the proposed MPPT algorithm, the proposed algorithm increases the traceability efficiency. The suggested algorithm has the fastest follow-up speed since the ? value decreases during the iteration exponentially.
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