自适应权重粒子群算法在阴影光伏发电最大功率点跟踪（MPPT）中的应用

在光伏发电系统中,光伏阵列往往会受到局部阴影现象的影响,造成系统的不稳定运行和输出功率的降低,且光伏阵列的P-U特性曲线会出现多峰值,常规最大功率点跟踪（MPPT）算法因其只能单峰寻优而不能完成对最大功率点的跟踪.粒子群优化（PSO）算法则有着良好的多峰全局寻优能力,被广泛应用在局部阴影的最大功率点跟踪中,但是PSO算法有着收敛速度不足和搜索精度低的缺点.为此,提出了基于自适应权重的粒子群优化（APSO）算法,即在运算过程中通过引入非线性动态惯性权重系数,有效地提高整体算法的全局搜索能力和局部改良能力.利用Matlab仿真,在恒定阴影和快速变化阴影2种条件下验证APSO算法的可行性.结果表明,APSO算法能够避免早熟收敛问题,可有效地提高算法的收敛速度和搜索精度.

Applications of Adaptive Particle Swarm Optimization Algorithm to MPPT of Shadow Photovoltaic Power Generation

In a photovoltaic power generation system,photovoltaic arrays tend to be affected by the phenomenon of partial shadow that will cause system instability and reduce its output with the P-U characteristic curve containing more than one peak,which makes the conventional algorithm of MPPT difficult to track the real maximum power point because of its single peak optimization.Particle swarm optimization algorithm is widely used in the maximum power point tracking of partial shadow for its good multimodal global optimization ability.The particle swarm algorithm,however,has shortcomings of low convergence speed and poor searching accuracy.So,the adaptive inertia weigh particle swarm optimization algorithm was proposed to solve these problems,in which the nonlinear dynamic inertia weight factor is introduced into the PSO evolution to reinforce the exploitation of global optimum of the PSO algorithm.To verify the feasibility of the algorithm under constant partial shading and rapidly changing partial shading,a simulation was conducted with Matlab,which shows that the novel algorithm can avoid the premature convergence effectively and the search capability is better.