局部阴影光伏发电系统中基于改进PSO的MPPT控制

光伏发电系统在局部阴影条件下，传统的最大功率点跟踪算法（maximum power point tracking，MPPT）容易陷入局部寻优，无法跟踪到全局最大功率点. 针对这一问题，本文提出了一种基于自适应学习因子粒子群算法的最大功率跟踪方法. 该方法在普通粒子群算法的基础上不断改变学习因子和权重系数，以提高算法收敛的速度和精度. 将其应用于局部阴影条件下的光伏发电系统最大功率点跟踪中，并在RT-LAB实时仿真平台中以两个接受不同光照强度的光伏阵列为例进行实时仿真验证. 仿真结果表明，两峰情况下本文所提出的自适应学习因子粒子群算法能够在0.298 s左右跟踪到全局最大功率点，普通粒子群算法需要约0.615 s，而扰动观察法陷入了局部最大功率点，本文所提算法能够有效提高系统的收敛速度和精度并且适用于多峰情况. 最后设置仿真算例验证本算法适用于光照突变的情况. 

MPPT Control of Partial Shadow Photovoltaic Generation System Based on Improved PSO Algorithm

Under partial shading condition, the traditional MPPT （maximum power point tracking） is suitable for local optimisation, which cannot track to the global MPP. To solve this problem, a maximum power tracking method based on adaptive learning factor particle swarm optimisation is proposed in this study. The learning factor and weight coefficient were constantly changed based on ordinary particle swarm optimisation to improve the speed and precision of the algorithm convergence. It was applied to the maximum power point tracking of photovoltaic system under partial shadow condition. In the RT-LAB environment, two different photovoltaic arrays with different illumination intensities were considered as examples to verify real-time effectiveness. The simulation results indicate that the proposed adaptive learning factor particle swarm algorithm can track to the global maximum power point in 0.298 s in two peaks conditions, while the ordinary PSO （particle swarm optimization） algorithm requires approximately 0.615 s, and the disturbance observation method suitable for local optimisation. These results prove that the proposed algorithm can effectively improve the convergence speed and accuracy and can be applied to multimodal situation. Finally, a simulation example was set up to verify that the algorithm was suitable for light mutation condition.