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<p>Harvesting the optimal power of solar photovoltaic (PV) cell under partial-shaded conditions (PSC) and dynamic irradiation conditions plays a vital role in efficiently utilizing solar power. This paper introduces new metaheuristic method named as arithmetic optimization algorithm (AOA) and is applied to the solar PV cell for searching the global maximum power point (GMPP) at variable irradiance. The suggested approach is modelled, and numerical simulations are performed on PV arrangements with various shading patterns using a MATLAB/Simulink environment. The proposed algorithm minimizes the oscillations in power during variable irradiation. It accurately tracks the GMPP at high tracking speed and efficiency for all shading patterns. The simulation results are compared with the P&amp;O, musical chair algorithm (MCA), and squirrel search algorithm (SSA) in terms of tracking time, efficiency, and maximum power extracted from the panel. The simulation results strongly suggest that the AOA has the highest tracking efficiency of &gt; 99.4% and the convergence time is &lt; 0.0018&#xa0;s under PSC. Statistical analyses are performed to observe the performance of AOA with P&amp;O, MCA, and SSA. The results show superiority of the proposed algorithm in terms of root mean square error, and relative error of the methods during various shading patterns. Further, an experimental setup was developed, and investigations are carried with uniform radiations. Experimental results suggest that AOA enhanced system parameters over MCA technique.</p>

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A novel AOA-based MPPT technique for 2S2P configuration of a solar PV system considering complex irradiations

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • B. S. S. Ganesh Pardhu,
  • Venkata Reddy Kota

摘要

Harvesting the optimal power of solar photovoltaic (PV) cell under partial-shaded conditions (PSC) and dynamic irradiation conditions plays a vital role in efficiently utilizing solar power. This paper introduces new metaheuristic method named as arithmetic optimization algorithm (AOA) and is applied to the solar PV cell for searching the global maximum power point (GMPP) at variable irradiance. The suggested approach is modelled, and numerical simulations are performed on PV arrangements with various shading patterns using a MATLAB/Simulink environment. The proposed algorithm minimizes the oscillations in power during variable irradiation. It accurately tracks the GMPP at high tracking speed and efficiency for all shading patterns. The simulation results are compared with the P&O, musical chair algorithm (MCA), and squirrel search algorithm (SSA) in terms of tracking time, efficiency, and maximum power extracted from the panel. The simulation results strongly suggest that the AOA has the highest tracking efficiency of > 99.4% and the convergence time is < 0.0018 s under PSC. Statistical analyses are performed to observe the performance of AOA with P&O, MCA, and SSA. The results show superiority of the proposed algorithm in terms of root mean square error, and relative error of the methods during various shading patterns. Further, an experimental setup was developed, and investigations are carried with uniform radiations. Experimental results suggest that AOA enhanced system parameters over MCA technique.