局部阴影条件下光伏电池多峰值最大功率点控制策略
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摘要
局部阴影条件下,光伏阵列的P-V曲线会呈现多个局域峰值,影响最大功率点跟踪(MPPT),传统MPPT算法只能跟踪单个功率峰值,在局部阴影输出功率多峰值条件下,该算法不能完成有效跟踪。粒子群算法(PSO)有较强多极点寻优能力,但易陷入局部最优解。针对此问题,在粒子群算法中引入模拟退火算法的Metropolis选择机制,在简化所需设置参数同时帮助粒子群算法有效跳出局部最优解。在控制过程中,采用主程序加嵌套迭代双重判定条件,保证粒子稳定前提下,收敛在最大功率点(MPP)附近。通过MATLAB对比仿真验证,表明该算法在局部遮阴情况下能较精确、快速地跟踪到最大功率点,有效提高光伏电池输出效率。
On the condition of partial shade, the PV curve of PV array will show multiple local peaks,impacting the maximum power point tracking(maximum power point tracking, MPPT). Traditional MPPT algorithm can only track a single power peak output power of more than partial shade in peak condition next,the algorithm could not be completed effectively tracked. Particle swarm optimization(particle swarm optimization, PSO) had good multi- pole optimization capability, but it was easy to fall into local optimal solution for this problem, the introduction of Metropolis simulated annealing algorithm selection mechanism in particle swarm algorithm, at the same time simplifying the help needed to set the parameters of particle swarm algorithm effectively out of local optima. In the control process, the proposed main plus nested iteration of this new thinking ahead to avoid particle convergence, to ensure the convergence of particles can be near to the maximum power point(MPP). Ultimately through MATLAB simulation of the algorithm showed that the algorithm in the case of partial shading could be more accurately and quickly track the maximum power point, effectively improve the output efficiency of photovoltaic cells.
On the condition of partial shade, the PV curve of PV array will show multiple local peaks,impacting the maximum power point tracking(maximum power point tracking, MPPT). Traditional MPPT algorithm can only track a single power peak output power of more than partial shade in peak condition next,the algorithm could not be completed effectively tracked. Particle swarm optimization(particle swarm optimization, PSO) had good multi- pole optimization capability, but it was easy to fall into local optimal solution for this problem, the introduction of Metropolis simulated annealing algorithm selection mechanism in particle swarm algorithm, at the same time simplifying the help needed to set the parameters of particle swarm algorithm effectively out of local optima. In the control process, the proposed main plus nested iteration of this new thinking ahead to avoid particle convergence, to ensure the convergence of particles can be near to the maximum power point(MPP). Ultimately through MATLAB simulation of the algorithm showed that the algorithm in the case of partial shading could be more accurately and quickly track the maximum power point, effectively improve the output efficiency of photovoltaic cells.
引文
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[2]李晶,窦伟,徐正国,等.光伏发电系统中最大功率点跟踪算法的研究[J].太阳能学报,2007,28(3):268-273.
[3]董密,杨建,彭可,等.光伏系统的零均值电导增量最大功率点跟踪控制[J].中国电机工程学报,2010,30(21):48-53.
[4]崔岩,白静晶.光伏阵列多峰最大功率点跟踪研究[J].电机与控制学报,2012,16(6):87-91.
[5]刘邦银,段善旭,刘飞,等.基于改进扰动观察法的光伏阵列最大功率点跟踪[J].电工技术学报,2009(6):91-94.
[6]党克,杨维湘,严干贵,等.光伏发电系统MPPT改进方法研究[J].电网与清洁能源,2013,29(4):91-95.
[7]高金辉,唐静.一种新型光伏系统最大功率跟踪算法的研究[J].电力系统保护与控制,2011,39(23):21-24.
[8]王福林,吴昌友,杨辉.用遗传算法求解约束优化问题时初始种群产生方法的探讨[J].东北农业大学学报,2004,35(5):608-611.
[9]徐梅,文士发,王福林,等.遗传算法求解约束优化问题时产生初始种群的改进方法[J].东北农业大学学报,2014,233(7):104-107.
[10]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(10):85-90.
[11]孙博,梅军,郑建勇.局部阴影条件下最大功率点跟踪改进算法[J].电力自动化设备,2014,4(1):115-119.
[12]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,2012,2(4):42-48.
[13]Kennedy J,Eberhart R.Particle swarm optimization[C].Proceedings of IEEE international conference on neural networks,1995:1942-1948.
[14]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:科学出版社,2009.
[15]Kirkpatrick S,Vecchi M P.Optimization by simmulated annealing[J].science,1983,220(4598):671-680.
[16]Van Laarhoven P J M,Aarts E H L.Simulated annealing[M].Springer Netherlands:D.Reidel Publishing Co.,1987.
[17]Johnson A A,Flegal J M.A modified conditional Metropolis-Hastings sampler[J].Computational Statistics&Data Analysis,2014,78:141-152.
[18]Vihola M.On the stability and ergodicity of adaptive scaling Metropolis algorithms[J].Stochastic Processes and their Applications,2011,121(12):2839-2860.