基于自适应变异粒子群算法的光伏MPPT控制研究

针对局部遮阴(PSC)时,光伏阵列输出P-U曲线出现多峰值现象,传统最大功率点追踪(MPPT)算法易陷入局部最优解,无法追踪到全局最优解的问题,该文提出基于自适应变异粒子群算法的光伏MPPT控制方法.在粒子群算法寻优过程中同步调整学习因子与惯性权重,以提高算法收敛速度与精度,同时引入变异机制以扩大粒子搜索范围,增强算法...

RESEARCH ON PHOTOVOLTAIC MPPT CONTROL BASED ON ADAPTIVE MUTATION PARTICLE SWARM OPTIMIZATION ALGORITHM

For the problem of partial shading condition（PSC）, there will be multi-peaks phenomenon in the output P-U curve of photovoltaic array, and the traditional maximum power point tracking（MPPT） algorithm can easily fall into local optimal solution and cannot track down the global optimal solution. Based on the above, a photovoltaic MPPT control method based on adaptive mutation particle swarm optimization is proposed in this paper. The learning factors and inertia weights are adjusted synchronously during particle swarm optimization in order to improve the convergence speed and accuracy of the algorithm. At the same time, the mutation mechanism is introduced to expand the particle search range and enhance the global optimization of the algorithm. Simulation results show that the algorithm can quickly and accurately track the maximum power point under uniform illumination, static and dynamic partial shading conditions, with faster convergence and higher steady-state accuracy than the standard particle swarm optimization.