局部阴影下光伏电池MPPT算法的研究

Research on MPPT Algorithm Under Local Shadow of Photovoltaic Cells

针对光伏阵列在局部阴影光照的条件下,其P/U特性曲线有多个峰值,传统的扰动观察法容易陷入局部最优的问题,从而无法追踪到最大功率点以及采用粒子群算法可有效解决多峰值最大功率点跟踪问题,但标准粒子群算法存在容易陷入波动、收敛速度较慢和稳定精度较差等问题,提出一种改进基于粒子群优化算法和扰动观察法相结合的优化算法。该方法引入了收缩学习因子和随机权重,再与传统扰动观察法相结合,可以提高算法的收敛速度和精度。通过MATLAB/Simulink仿真,结果表明,所提的方法具有追踪到的最大功率波动范围小,跟踪精度高,能以更快的速度达到最大功率点等优点。

Under the condition of local shadow illumination,the P/U characteristic curve of photovoltaic array has multiple peaks.The traditional disturbance observation method is easy to fall into the problem of local optimization,so it can not track the maximum power point;Particle swarm optimization algorithm can effectively solve the problem of multi peak maximum power point tracking,but the standard particle swarm optimization algorithm has some problems,such as easy to fall into local optimization,slow convergence speed and poor stability accuracy.Therefore,an improved optimization algorithm based on the combination of particle swarm optimization algorithm and disturbance observation method is proposed.This method introduces contraction learning factor and random weight,and then combined with the traditional disturbance observation method,the convergence speed and accuracy of the algorithm can be improved.Through MATLAB/Simulink simulation,the results show that the proposed method has the advantages of small power fluctuation range,high tracking accuracy,and can reach the maximum power point at a fast speed.