基于自适应粒子群算法的MPPT控制策略

MPPT control strategy based on adaptive particle swarm optimization algorithm

最大功率点追踪(MPPT)技术的使用使得光伏组件的转换效率大幅提升,在有遮挡的情况下,光伏阵列会呈现多峰的输出曲线,传统的MPPT方法容易陷入局部最大功率点,无法追踪到全局的最大功率点。全局算法中,传统粒子群算法存在收敛速度慢、种群容易早熟、对初始条件敏感等问题,为解决这一问题,提出了一种全新的基于自适应粒子群(adaptive particle swarm optimization,APSO)算法MPPT控制策略。通过引入自适应参数算法和随机粒子加快粒子群的收敛速度,既解决了传统方法无法找寻到全局最大点、寻找速度慢的问题,又解决了传统粒子群算法随机性大、收敛速度慢、会产生较大震荡的问题。在Matlab/Simulink上搭建光伏系统模型,在固定辐照度和动态辐照度的条件下对所提算法进行仿真,结果表明:相对于传统方法和传统粒子群算法,所提出的MPPT控制策略在追踪精度、追踪速度和响应速度上均有大幅提升,能够提升光伏组件的转换效率。

The use of maximum power point tracking(MPPT)technology has greatly improved the conversion efficiency of photovoltaic modules.In the case of obstruction,the photovoltaic array will show a multi-peak output curve.The traditional MPPT method is easy to fall into the local maximum power point and cannot track the global maximum power point.Among the global algorithms,ordinary particle swarm algorithms have some problems such as slow convergence speed,premature populations,and sensitivity to initial conditions.To solve this problem,this paper proposes a new algorithm based on adaptive particle swarm optimization(APSO)MPPT control strategy.By introducing adaptive parameter algorithm and random particles to speed up the convergence speed of the particle swarm,it not only solves the problem that the INC method cannot find the global maximum point and the search speed is slow,but also solves the problem of the large randomness of the PSO algorithm and the slow convergence speed and the problem of greater volatility.The photovoltaic system model is built in Matlab/Simulink,and the proposed algorithm is simulated under the conditions of fixed irradiance and dynamic irradiance.The results show that:compared with the traditional method and traditional particle swarm optimization algorithm,the MPPT control strategy proposed in this paper can greatly improve the tracking accuracy,tracking speed and response speed,and can improve the conversion efficiency of photovoltaic modules.