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<p>Tracking the peak power output of solar photovoltaic modules poses a significant challenge in contemporary times, that too under variable climatic conditions. Despite the availability of various Maximum Power Point Tracking (MPPT) methods, each method carries its own set of limitations. Many of these constraints can be effectively addressed by leveraging a suitable metaheuristic algorithm. In this context, a Particle Swarm Optimization Memetic Algorithm (PSOMA) is proposed as a powerful tool for accelerating convergence towards the maximum power point and enhancing the tracking process. Furthermore, the proposed algorithm incorporates the impact of changes in tilt angle, thereby augmenting its efficacy. Simulation results demonstrate that the proposed method exhibits superior tracking capabilities compared to conventional MPPT methods and various other MPPT algorithms. The convergence time is also greatly reduced by the proposed method. An efficiency of 99.91% and a convergence time of 8.5&#xa0;ms is achieved by this algorithm. The efficiency remains almost constant for different irradiance levels which is demonstrated by simulation results. Additionally, hardware experimentation validates the robustness of the developed algorithm.</p>

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MPPT of solar PV systems using PSO memetic algorithm considering the effect of change in tilt angle

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Loganathan V,
  • Jothi Swaroopan N M

摘要

Tracking the peak power output of solar photovoltaic modules poses a significant challenge in contemporary times, that too under variable climatic conditions. Despite the availability of various Maximum Power Point Tracking (MPPT) methods, each method carries its own set of limitations. Many of these constraints can be effectively addressed by leveraging a suitable metaheuristic algorithm. In this context, a Particle Swarm Optimization Memetic Algorithm (PSOMA) is proposed as a powerful tool for accelerating convergence towards the maximum power point and enhancing the tracking process. Furthermore, the proposed algorithm incorporates the impact of changes in tilt angle, thereby augmenting its efficacy. Simulation results demonstrate that the proposed method exhibits superior tracking capabilities compared to conventional MPPT methods and various other MPPT algorithms. The convergence time is also greatly reduced by the proposed method. An efficiency of 99.91% and a convergence time of 8.5 ms is achieved by this algorithm. The efficiency remains almost constant for different irradiance levels which is demonstrated by simulation results. Additionally, hardware experimentation validates the robustness of the developed algorithm.