基于ISSA_GWO算法的局部阴影下光伏MPPT的研究

Research on Photovoltaic MPPT under Local Shadow Based on ISSA_GWO Algorithm

摘要: 针对局部阴影下传统光伏系统最大功率点跟踪(MPPT)算法易陷入局部最优的问题，提出了一种新的控制模型——ISSA_GWO模型。该模型融合了樽海鞘算法的全局搜索能力和灰狼算法的局部开发特性。另外，该模型通过引入动态平衡因子调节SSA算法的全局和局部搜索权重，并结合差分进化策略优化领导者位置更新，从而有效提升了多峰场景下的寻优成功率。该算法与PSO、SSA、GWO算法在不同辐照条件下进行仿真对比实验，验证了ISSA_GWO算法在复杂阴影环境下具有更快的跟踪速率和更高的收敛精度，同时也具有更高的鲁棒性与效率，该算法为复杂阴影环境下的光伏系统提供更高效的MPPT解决方案。

Abstract: In order to solve the problem that the traditional MPPT algorithm of PV system is easy to fall into local optimal under local shadow, a new control model, ISSA_GWO model, is proposed. This model combines the global search ability of Salpa algorithm with the local development characteristic of gray Wolf algorithm. The global/local search weights of the SSA algorithm are adjusted by introducing dynamic balance factors, and the leader position update is optimized with differential evolution strategy, thus effectively improving the search success rate in multi-peak scenarios. The simulation and comparison experiments of this algorithm with PSO, SSA and GWO algorithms under different irradiation conditions verify that ISSA_GWO algorithm has faster tracking rate and higher convergence accuracy in complex shadow environment, and also has higher robustness and efficiency. This algorithm provides a more efficient MPPT solution for photovoltaic systems in complex shadow environment.