阴影条件下基于集体智慧的光伏系统最大功率跟踪

光伏阵列通常受局部阴影的影响，导致系统输出功率较低。这主要归咎于光伏阵列的功率-电压特性曲线在阴影条件下具有多个功率峰值，而常规最大功率跟踪算法易陷入局部最优。设计了一种新颖的MPPT算法，即基于动态领导的集体智慧。与传统启发式算法不同，该算法由多个子优化器组成，每个优化器同时进行全局寻优，并选择适应度函数最小(最优解)的子优化器作为其他子优化器的领导者进行后续引导。三种算例(恒定气候条件、时变气候条件和大型光伏电站)下的Matlab/Simulink仿真结果显示，所提算法与导纳增量控制法和其余五种经典的启发式算法相比，DLCI能在PSC下实现最快速与稳定的全局最大功率跟踪。最后，基于dSpace的硬件在环实验验证了所提算法的硬件实施可行性。

Collective intelligence-based maximum power point tracking of PV systems under partial shading condition

PV arrays are usually affected by a partial shading condition, which leads to a relatively low power production. This is because the power-voltage curve of a PV system contains multiple peaks while the traditional Maximum Power Point Tracking (MPPT) algorithm is easily trapped at the Local Maximum Power Point (LMPP). Hence, a novel MPPT approach is provided, i.e., Dynamic Leader-based Collective Intelligence (DLCI). Unlike traditional meta-heuristic algorithms, this algorithm has a multiple sub-optimizer which seeks the optimum independently. Then, the current best optimum will be chosen as the dynamic leader to guide the other sub-optimizers thereafter. Three case studies are carried out, i.e., constant climate conditions, varying climate conditions, and a large-scale photovoltaic station. Simulation outcomes of Matlab/Simulink prove that DLCI outperforms the traditional Incremental Conductance (INC) and five other typical meta-heuristic algorithms. It can achieve the fastest and most stable global MPPT. Lastly, a dSpace based Hardware-In-the-Loop (HIL) test is carried out to validate the implementation feasibility of the DLCI algorithm.This work is supported by the National Natural Science Foundation of China (No. 61963020).