复杂遮荫环境下,光伏阵列P-U曲线呈现多个峰值,常规最大功率跟踪（MPPT）算法不能精确、快速地找到最大功率点。因此提出一种改进白鲸算法（IBWO）与改进扰动观察法（IP&O）结合的MPPT控制策略。IBWO采用Henon混沌映射增加种群多样性,引入自适应步长因子和最优个体引导策略,增强跳出局部最优和进行全局寻优的能力,进而提高追踪精度。首先IBWO追踪到最大功率点周围,再利用IP&O提高追踪的精度。仿真实验表明,与金枪鱼算法（TSO）和白鲸算法（BWO）相比,IBWO-IP&O算法在各种复杂遮荫环境下均具有更快的追踪速度、更高的追踪精度和更低的功率振荡。
Abstract
The PV array P-U curves show multiple peaks under complex shading environments, and the conventional maximum power tracking （MPPT） algorithm cannot find the maximum power point accurately and quickly. Therefore, an MPPT control method combining the improved beluga whale optimization（IBWO） algorithm and improved perturbation observation （IP&O） is proposed. IBWO adopts Henon chaotic mapping to increase the diversity of the population, and introduces an adaptive step factor and an optimal individual steering strategy, which enhances the ability of jumping out of the local optimum and global optimization searching, and then improves the tracking accuracy. IBWO tracks firstly around the maximum power point, and then IP&O is utilized to improve the tracking accuracy. Simulation experiments show that compared with the TSO and BWO algorithms, the IBWO-IP&O algorithm has faster tracking speed, higher tracking accuracy, and lower power oscillation in various complex shading environments.
关键词
太阳电池 /
最大功率点跟踪 /
光伏发电 /
自适应步长因子 /
混沌映射
Key words
solar cell /
maximum power point tracking /
photovoltaic power generation /
adaptive step factor /
chaotic mapping
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基金
辽宁省教育厅科研项目（LJKZ0332）; 辽宁工程技术大学鄂尔多斯研究院校地科技合作培育项目（YJY-XD-2023-004）
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