在局部遮阴条件下光伏阵列的输出功率存在多个峰值，传统的最大功率点追踪(MPPT)技术在寻优过程中易陷入局部峰值，难于快速准确地追踪到最大功率点。针对这一问题，提出了一种改进麻雀搜索算法应用于光伏MPPT技术。首先，借鉴于乌鸦搜索算法引入了飞行步长，并采用一种动态递变规则调节飞行步长，增强加入者的探索能力，解决了麻雀搜索算法在低维下寻优精度低的问题；其次，设计了一种自适应规则应用于发现者位置更新中，同时通过边界处理策略约束麻雀位置更新，保证了麻雀搜索算法能够有效地解决搜索范围较小情况下的寻优问题；最后，在MATLAB中对6个基准函数进行测试验证，并将改进的算法应用于非均匀光照条件下光伏阵列的最大输出功率点追踪中进行仿真。结果表明，所提出的改进麻雀搜索算法具有较快的收敛速度和较高的追踪精度，在寻优过程中能有效避免陷入局部峰值并快速精确地追踪到最大功率点。

Under partial shading conditions, the output power of photovoltaic arrays has multiple peaks. The traditional maximum power point tracking (MPPT) technology is easy to fall into local peaks during the optimization process, and it is difficult to quickly and accurately track the maximum power point. To solve this problem, an improved sparrow search algorithm is proposed to apply to photovoltaic MPPT technology. The flight step is introduced based on the crow search algorithm, and a dynamic gradient rule is used to adjust the flight step to enhance the exploration ability of the participants, and solve the problem of low optimization accuracy of the sparrow search algorithm in low dimensions. A selfadaption rule is designed and applied to the update of the finder′s position, and the sparrow′s position update is constrained by the boundary processing strategy, which ensures that the sparrow search algorithm can effectively solve the optimization problem in the case of a small search range. The six benchmark functions are tested and verified in MATLAB, and the improved algorithm is applied to the maximum output power point tracking of photovoltaic arrays under uneven illumination conditions for the simulation. The results show that the proposed improved sparrow search algorithm has fast convergence speed and high tracking accuracy, and can effectively avoid falling into local peaks and track the maximum power point quickly and accurately during the optimization process.

国家自然科学基金资助项目（62062048）