随着可持续发展理念的提出，传统能源转型问题已被全世界广泛关注，在众多新能源体系中，太阳能作为储备广泛、绿色环保、安全便捷的可再生能源成为了发展的重点目标。然而，光伏系统的能量转换率较低，且前期需要投入大量的资金，限制了光伏产业的大范围推广。为了最大程度地输出光伏发电系统产生的电能，推出了最大功率点跟踪（maximum power point tracking，MPPT）策略以减少有效功率的损失。

      作为光伏系统最重要的单元器件，本文首先对光伏电池的基本特性进行研究。分别构建了光照均匀情况与发生局部遮阴情况时的光伏阵列仿真模型，重点分析了输出特性曲线极值点情况与动态环境中电池参数的变化情况；将两种光照情况下所对应的常见MPPT策略进行分类阐述，指出了在应用过程中原始策略存在的问题；针对处于均匀光照情况下的光伏系统提出了滞环控制型变步长扰动观察算法（hysteresis controlled variable step perturbation and observation，HP&O），针对局部遮阴情况下提出了一种基于自适应布谷鸟搜索（adaptive cuckoo search，ACS）和扰动观察（perturbation and observation，P&O）的混合算法。

当光伏系统处于均匀光照下时，提出的HP&O算法可自动调节扰动信号的步长大小，使跟踪兼顾快速性与稳定性；若光照强度产生变化，滞环控制的引入可将工作点限制于功率滞环区间内，避免工作点因误判情况而发生偏移；变步长公式中新添的反正切函数可抑制功率变化量的突增，阻止尖峰步长的产生。当光伏阵列发生局部遮阴时启用ACS-P&O混合算法对全局最大功率点进行跟踪，在前期全局搜索阶段应用的是ACS算法，该改进算法将布谷鸟搜索算法中的切换概率与Lévy飞行的步长系数自适应化，并且加入了边界个体的处理策略，大幅度提高了算法跟踪速度与全局性能；在后期的局部搜索阶段切换为小步长P&O算法，防止智能算法中因随机性的存在使得收敛阶段功率输出不稳定。

      最后，为验证本文所提方法使光伏发电系统MPPT效率得到了提升，在MATLAB/simulink软件中对光伏发电系统的仿真模型进行搭建。分别将所提算法在不同外界环境下进行对比验证，结果表明了改进算法在任何状态下都可以成功跟踪到最大功率点，光伏系统的发电效率也得到了显著提升。

With the introduction of the concept of sustainable development, the issue of traditional energy transformation has been widely focused on around the world. Among the many new energy systems, solar energy has become a key target for development as a renewable energy source with extensive reserves, green, safe and convenient. However, the low energy conversion rate of photovoltaic systems and the large upfront capital investment required limit the widespread diffusion of the industry. In order to maximise the output of the power generated by the PV system, a maximum power point tracking (MPPT) strategy has been introduced to reduce the loss of effective power.

As the most important single component of a photovoltaic system, this paper begins with a study of the basic characteristics of photovoltaic cells. Simulation models of PV arrays with uniform illumination and partial shading were constructed, focusing on the extreme value point of the output characteristic curve and the variation of cell parameters in the dynamic environment; the common MPPT strategies corresponding to the two lighting situations are categorized and elaborated, and the problems of the original algorithmic strategies in the application process are pointed out; a hysteresis controlled variable step perturbation and observation (HP&O) algorithm is proposed for photovoltaic systems under uniform illumination, A hybrid algorithmic strategy of adaptive cuckoo search (ACS) and perturbation and observation (P&O) is proposed for the case of local shading.

The proposed HP&O algorithm automatically adjusts the step size of the perturbation signal when the PV system is under uniform illumination, so that the tracking balances speed and stability; the introduction of hysteresis control to limit the operating point to the power hysteresis interval if the light intensity changes, avoiding shifts in the operating point due to misjudged conditions; the new inverse tangent function added to the variable step equation suppresses sudden increases in the amount of power change and stops spikes in the step size. The ACS-P&O hybrid algorithm is enabled to track the global maximum power point when local shading of the PV array occurs. The ACS algorithm is applied in the preliminary global search phase, and this improved algorithm adapts the switching probability in the cuckoo search algorithm with the step coefficients of Lévy flight, and incorporates a boundary individual processing strategy to substantially improve the algorithm tracking speed and global performance; switching to a small-step P&O algorithm in the later stages of the local search prevents the presence of randomness in the intelligent algorithm from making the power output unstable in the convergence phase.

Finally, to demonstrate that the proposed method has improved the MPPT efficiency of the PV power system, a simulation model of the PV power system is created in MATLAB/simulink software.The proposed algorithms are compared and validated in different external environments, the results show that the improved algorithm can successfully track to the maximum power point in any state and that the power generation efficiency of the PV system has been dramatically enhanced.

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