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<p>Driven by the global energy transformation and the goal of ‘double carbon’, photovoltaic power generation is becoming an important force to improve the energy structure with its clean, efficient and renewable characteristics. However, under complex working conditions such as local shadow and multi-peak output, the traditional maximum power point tracking (MPPT) algorithm often encounters local extreme dilemma, and it is difficult to fully tap the potential of photovoltaic power generation system. Therefore, based on the equivalent modelling of photovoltaic cells and the multi-peak characteristics of the output curve under uneven illumination, this paper proposes a hybrid MPPT control scheme combining the Improved Horned Lizard Optimisation Algorithm (IHLOA) and the variable step-size disturbance observation method (P&amp;O) to achieve fast and high-precision global optimisation. Based on the characteristics of the HLOA population, we have implemented improvements, such as chaotic mapping, opposition-based learning, and multi-population cooperation. The results show that the IHLOA-PO algorithm can track global optimal power points such as 846.6 and 628.5&#xa0;W within 0.08 to 0.10&#xa0;s, with a tracking rate as high as 99.97 to 100%, significantly outperforming other comparison algorithms such as IHLOA, PSO, and FA. Finally, aiming at the multi-peak problem under complex illumination, this paper integrates the improvement of photovoltaic modelling and intelligent algorithm and constructs an efficient and robust MPPT control scheme, which provides practical technical reference for distributed photovoltaic systems to deal with complex environmental conditions and contributes new ideas and methods to realise low-carbon energy and green transformation.</p>

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Research on Maximum Power Point Tracking of Photovoltaic Systems Under Complex Conditions Based on an Improved Horned Lizard Optimisation Algorithm

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Xuejing Sun,
  • Heng Zhong

摘要

Driven by the global energy transformation and the goal of ‘double carbon’, photovoltaic power generation is becoming an important force to improve the energy structure with its clean, efficient and renewable characteristics. However, under complex working conditions such as local shadow and multi-peak output, the traditional maximum power point tracking (MPPT) algorithm often encounters local extreme dilemma, and it is difficult to fully tap the potential of photovoltaic power generation system. Therefore, based on the equivalent modelling of photovoltaic cells and the multi-peak characteristics of the output curve under uneven illumination, this paper proposes a hybrid MPPT control scheme combining the Improved Horned Lizard Optimisation Algorithm (IHLOA) and the variable step-size disturbance observation method (P&O) to achieve fast and high-precision global optimisation. Based on the characteristics of the HLOA population, we have implemented improvements, such as chaotic mapping, opposition-based learning, and multi-population cooperation. The results show that the IHLOA-PO algorithm can track global optimal power points such as 846.6 and 628.5 W within 0.08 to 0.10 s, with a tracking rate as high as 99.97 to 100%, significantly outperforming other comparison algorithms such as IHLOA, PSO, and FA. Finally, aiming at the multi-peak problem under complex illumination, this paper integrates the improvement of photovoltaic modelling and intelligent algorithm and constructs an efficient and robust MPPT control scheme, which provides practical technical reference for distributed photovoltaic systems to deal with complex environmental conditions and contributes new ideas and methods to realise low-carbon energy and green transformation.