针对传统的最大功率点追踪（Maximum Power Point Tracking,MPPT）算法陷入局部极值不能找到最大功率点（Maximum Power Point，MPP）以及传统的蝴蝶优化算法（Butterfly Optimization Algorithm,BOA）存在收敛速度慢和搜索震荡较大等问题，提出一种改进的蝴蝶优化算法（Improved Butterfly Optimization Algorithm,IBOA）结合电导增量法（Conductance Increment Method,INC）的复合MPPT追踪方法。在IBOA中，引入自适应动态转换概率来平衡算法的全局与局部搜索，然后在全局搜索阶段引入Levy飞行策略，使蝴蝶个体广泛分布于搜索空间中，提高全局寻优能力；同时在局部搜索中设置新的寻优对象，并通过贪婪算法进行筛选保留，提高局部搜索的能力。当系统位于MPP附近时，利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明，在静态和动态阴影下与BOA、PSO算法进行对比，所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
英文摘要:
An improved butterfly optimization algorithm (IBOA) combined with the conductance increment method (INC) is proposed to address the limitations of traditional maximum power point tracking (MPPT) algorithms, which often get trapped in local extrema and fail to find the maximum power point (MPP), as well as the slow convergence speed and large search oscillations of the traditional butterfly optimization algorithm (BOA). In IBOA, an adaptive dynamic transition probability is introduced to balance the global and local search capabilities of algorithm. The Levy flight strategy is then incorporated during the global search phase to enable the butterfly individuals to explore the search space extensively and enhance the global optimization capability. Additionally, a new optimization target is set for the local search, and a greedy algorithm is employed for selection and retention, thus improving the effectiveness of the local search. When the system is near the MPP, the strong local search capability of INC is leveraged to rapidly and accurately converge to the MPP and achieve stable power output. Simulation results demonstrate that compared to BOA and PSO algorithms under static and dynamic shading conditions, the proposed algorithm exhibits faster tracking speed, higher tracking efficiency, and stronger robustness.