基于改进粒子群优化算法光伏阵列多峰值MPPT的研究
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摘要
光伏阵列局部处于阴影时,其功率输出会呈现多峰值特征,将造成传统的MPPT算法跟踪失效。文章针对标准粒子群算法(PSO)在实现多峰值MPPT控制时,存在容易进入局部最优、收敛速度较慢和跟踪精度较低等问题,提出了一种基于改进PSO算法的多峰值MPPT控制算法。该方法把非线性变化的变异策略引入到PSO算法中,在显著提高跟踪速度的前提下,扩大了粒子的搜索范围,从而增强了全局寻优能力。仿真与实验结果表明,与传统的PSO方法相比,文章所提出的方法在均匀光照、静态阴影和动态阴影下,均能快速精准地实现对全局最大功率点的跟踪和控制,在一定程度上提高了光伏阵列的发电效率。
Aiming at the problem of multi-peak characteristic of power output of photovoltaic array when it is partially shaded, which results in tracking failure of traditional maximum power point tracking(MPPT) algorithm, and the standard particle swarm optimization(PSO)is easy access to local optimum, having slow convergence speed and low tracking accuracy when implementing multi-peak MPPT control, a multi-peak MPPT control algorithm based on improved PSO algorithm is proposed in this paper. In this method, the mutation strategy of non-linear change is introduced into PSO algorithm, which expands the searching range of particles and enhances the ability of global optimization on the premise of significantly improving the tracking speed. Compared with the traditional PSO method,the simulation and experimental results show that the proposed method can track and control the global maximum power point quickly and accurately under uniform illumination, static shadow and dynamic shadow, which improves the output efficiency of PV array to some extent.
Aiming at the problem of multi-peak characteristic of power output of photovoltaic array when it is partially shaded, which results in tracking failure of traditional maximum power point tracking(MPPT) algorithm, and the standard particle swarm optimization(PSO)is easy access to local optimum, having slow convergence speed and low tracking accuracy when implementing multi-peak MPPT control, a multi-peak MPPT control algorithm based on improved PSO algorithm is proposed in this paper. In this method, the mutation strategy of non-linear change is introduced into PSO algorithm, which expands the searching range of particles and enhances the ability of global optimization on the premise of significantly improving the tracking speed. Compared with the traditional PSO method,the simulation and experimental results show that the proposed method can track and control the global maximum power point quickly and accurately under uniform illumination, static shadow and dynamic shadow, which improves the output efficiency of PV array to some extent.
引文
[1]吴春华,周笛青,李智华,等.光伏组件热斑诊断及模糊优化控制方法[J].中国电机工程学报,2013,33(36):50-61.
[2]刘艳莉,周航,程泽.基于粒子群优化的光伏系统MPPT控制方法[J].计算机工程,2010,36(15):265-267.
[3]袁晓玲,陈宇.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[J].中国电力,2013,46(10):85-90.
[4]朱艳伟,石新春,但扬清,等.粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J].中国电机工程学报,2012,32(4):42-49.
[5]方伟中,张勇军.基于粒子群优化神经网络光伏并网最大功率点跟踪技术研究[J].可再生能源,2018,36(8):1181-1185.
[6]徐岩,梁大为,李春来,等.基于自然选择的改进粒子群优化MPPT算法[J].电气应用,2016,35(16):34-37.
[7] Hans J,Solheim Hallvard G. Measurement and simulation of hot spots in solar cells[J].Energy Procedia,2013,38:183-189.
[8]戚军,张晓峰,张有兵,等.考虑阴影影响的光伏阵列仿真算法研究[J].中国电机工程学报,2012,32(32):131-138.
[9]高立群,吴沛峰,邹德旋.基于变异策略的粒子群算法[J].东北大学学报,2010,31(11):1530-1533.
[2]刘艳莉,周航,程泽.基于粒子群优化的光伏系统MPPT控制方法[J].计算机工程,2010,36(15):265-267.
[3]袁晓玲,陈宇.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[J].中国电力,2013,46(10):85-90.
[4]朱艳伟,石新春,但扬清,等.粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J].中国电机工程学报,2012,32(4):42-49.
[5]方伟中,张勇军.基于粒子群优化神经网络光伏并网最大功率点跟踪技术研究[J].可再生能源,2018,36(8):1181-1185.
[6]徐岩,梁大为,李春来,等.基于自然选择的改进粒子群优化MPPT算法[J].电气应用,2016,35(16):34-37.
[7] Hans J,Solheim Hallvard G. Measurement and simulation of hot spots in solar cells[J].Energy Procedia,2013,38:183-189.
[8]戚军,张晓峰,张有兵,等.考虑阴影影响的光伏阵列仿真算法研究[J].中国电机工程学报,2012,32(32):131-138.
[9]高立群,吴沛峰,邹德旋.基于变异策略的粒子群算法[J].东北大学学报,2010,31(11):1530-1533.
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