基于卡尔曼滤波和改进粒子群的MPPT算法

MPPT Algorithm Based on Kalman Filter and Improved Particle Swarm Optimization Algorithm

针对常规的粒子群算法(particle swarm optimization algorithm, PSO)在应用中存在数据波动大、数据精确采集难度高和追踪速度慢等问题,课题组提出了基于卡尔曼滤波和改进粒子群优化算法的光伏最大功率点跟踪(maximum power point tracking, MPPT)控制器技术,并结合卡尔曼滤波、恒定电压和冒泡排序等方法进行优化。实验结果表明课题组提出的算法可以快速、准确地跟踪到最大功率点,提高了MPPT的响应速度和精度,提高了光伏发电的利用效率。

Aiming at the problems of large data fluctuation, high difficulty in accurate data collection and slow tracking speed in the application process of particle swarm optimization algorithm, the maximum power point(MPPT) controller was proposed based on Kalman filter and improved particle swarm optimization algorithm.Optimization was carried out combined with Kalman filtering, constant voltage method, bubble sorting and other methods. The experimental results show that the proposed method can quickly and accurately track the maximum power point, improve the response speed and response accuracy of MPPT, and improve the utilization efficiency of photovoltaic power generation.