基于改进粒子群算法的光伏系统MPPT控制研究

Research on MPPT Control of Photovoltaic System Based on Improved Particle Swarm Optimization

光伏(PV)阵列输出的功率-电压特性曲线在部分阴影条件下具有多个峰值,传统的最大功率点跟踪算法,无法准确跟踪光伏系统的全局最大功率点而且效率低下。由于粒子群优化(PSO)算法非常适合解决多极优化问题,因此,提出了一种随机惯性权重的PSO算法来实现全局最大功率点跟踪。通过改善传统PSO算法的惯性权重系数并优化粒子的搜索顺序,可以减少迭代次数,从而在更短的时间内找到MPP(最大功率点),以确保准确的跟踪最大功率,使系统始终保持最高效率运行。最后,搭建了局部阴影条件下的光伏阵列仿真模型,对提出的算法进行了仿真验证,并与传统的扰动观察法对比分析,仿真结果表明,相较于传统的扰动观察法,利用改进的智能算法,有效地解决了光伏系统在局部阴影条件下准确的追踪系统全局最大功率点的问题,并且加快了系统控制器的响应速度、有效地抑制震荡并且提高了追踪效率。

Since the power-voltage characteristic curve of photovoltaic(PV) array has multiple peaks under partial shadow conditions, the traditional maximum power point tracking algorithm cannot accurately track the global maximum power point of the photovoltaic system, and the efficiency is low. Since particle swarm optimization(PSO) algorithm is very suitable for solving the problem of multipole optimization, a PSO algorithm with random inertia weight is proposed to realize global maximum power point tracking. This paper proposes a PSO algorithm based on random inertia weight to achieve global maximum power tracking. By improving the inertia weight coefficient of the traditional PSO algorithm and optimizing the particle search order, the number of iterations can be reduced, and the maximum power point(MPP) can be found in a shorter time to ensure accurate tracking of the maximum power, so that the system always maintains the highest efficiency.Finally, the simulation model of photovoltaic array under local shadow was built, and the proposed algorithm was verified by simulation and compared with the traditional disturbance observation method. The simulation results show that compared with the traditional disturbance observation method, the improved intelligent algorithm can effectively solve the problem of tracking the global maximum power point of photovoltaic system under local shadow, accelerate the response speed of the system controller, effectively restrain the oscillation and improve the tracking efficiency.