Inertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi‐peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Lévy flight strategy with DCWQPSO algorithm. Lévy flight is a non‐Gaussian random process. The algorithm introduces Lévy flight strategy to change the mutation formula of particles and uses the characteristics of Lévy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast‐tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness.

中文翻译：

---

改进的量子粒子群光伏多峰mPPT方法与Lévy飞行相结合

惯性自适应粒子群优化算法（DCWQPSO）可以有效地解决粒子陷入局部极值的问题。但是在粒子演化的后期，粒子仍然有可能跌落到局部极值。当将其应用于光伏多峰最大功率跟踪（MPPT）时，跟踪效率不仅会降低，而且在突然跟踪光伏光强度的情况下可能会导致跟踪失败。针对上述问题，提出了一种将Lévy飞行策略与DCWQPSO算法相结合的光伏最大功率跟踪（MPPT）控制算法。列维飞行是非高斯随机过程。该算法引入了Lévy飞行策略来改变粒子的变异公式，并利用Lévy飞行的短步和偶尔长步跳跃搜索的特征来提高算法种群中粒子的多样性。本文提出的算法提高了粒子的多样性，提高了算法的收敛精度和速度，克服了DCWQPSO算法的缺陷。仿真结果表明，本文提出的MPPT控制算法跟踪速度快，精度高，可以有效提高不确定环境下光伏发电系统的最大功率跟踪效率和动态质量，并且具有良好的鲁棒性。