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<p>The performance of a solar photovoltaic (PV) module or array is significantly affected by partial shading conditions (PSC), which reduce the output power and shorten the lifespan of the PV system due to the formation of hotspots on the PV cells. These conditions lead to multiple peaks on the power-voltage (P–V) curve, which cannot be accurately tracked using classical maximum power point tracking (MPPT) methods. Consequently, metaheuristic-based optimization techniques are employed to locate the global maximum power point (GMPP). This paper addresses the challenges posed by PSC in a standalone PV system by implementing a recent optimization algorithm known as the tumoral angiogenesis optimizer (TAO). The proposed MPPT approach is validated under various partial shading scenarios and benchmarked against existing MPPT algorithms, including particle swarm optimization (PSO), horse herd optimization algorithm (HOA), salp swarm algorithm (SSA), and osprey optimization algorithm (OOA). Simulations are conducted using MATLAB R2021. The performance is evaluated in terms of output power, voltage, and duty cycle, demonstrating the TAO algorithm’s capability to rapidly and accurately track the GMPP. Moreover, the tracking efficiency, convergence time, and power oscillations of each MPPT method are analyzed. The proposed method achieves an average tracking efficiency of 99.5%, a convergence time of 0.1 s, and negligible power oscillation. Compared to other methods—PSO (95%), HOA (94.2%), SSA (94.4%), and OOA (95.3%)—the TAO-based MPPT demonstrates superior performance in enhancing energy harvesting under complex PSC scenarios.</p>

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A tumoral angiogenesis optimizer-based high-speed MPPT method for solar PV systems under partial shading

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Salam J. Yaqoob,
  • Salah Kamel,
  • Francisco Jurado

摘要

The performance of a solar photovoltaic (PV) module or array is significantly affected by partial shading conditions (PSC), which reduce the output power and shorten the lifespan of the PV system due to the formation of hotspots on the PV cells. These conditions lead to multiple peaks on the power-voltage (P–V) curve, which cannot be accurately tracked using classical maximum power point tracking (MPPT) methods. Consequently, metaheuristic-based optimization techniques are employed to locate the global maximum power point (GMPP). This paper addresses the challenges posed by PSC in a standalone PV system by implementing a recent optimization algorithm known as the tumoral angiogenesis optimizer (TAO). The proposed MPPT approach is validated under various partial shading scenarios and benchmarked against existing MPPT algorithms, including particle swarm optimization (PSO), horse herd optimization algorithm (HOA), salp swarm algorithm (SSA), and osprey optimization algorithm (OOA). Simulations are conducted using MATLAB R2021. The performance is evaluated in terms of output power, voltage, and duty cycle, demonstrating the TAO algorithm’s capability to rapidly and accurately track the GMPP. Moreover, the tracking efficiency, convergence time, and power oscillations of each MPPT method are analyzed. The proposed method achieves an average tracking efficiency of 99.5%, a convergence time of 0.1 s, and negligible power oscillation. Compared to other methods—PSO (95%), HOA (94.2%), SSA (94.4%), and OOA (95.3%)—the TAO-based MPPT demonstrates superior performance in enhancing energy harvesting under complex PSC scenarios.