APSO算法提取染料敏化太阳电池参数的研究

Parameter extraction of dye-sensitized solar cells using adaptive particle swarm optimization algorithm

利用MATLAB软件对染料敏化太阳电池的输出特性进行了建模与仿真实验,基于电池的等效电路方程建立了寻优目标函数。采用APSO算法精确提取了染料敏化太阳电池的内部参数,讨论了权重因子策略和种群规模对各参数提取结果的影响。各参数的提取值与理论值相对误差在1%以内,自适应权重因子策略可明显提高APSO算法提取染料敏化电池参数的求解精度。将单纯形算法的参数提取结果与APSO算法进行了比较,二者均与理论值吻合,而单纯形算法对初始值表现出较高的依赖性,APSO方法具有更高寻优效率和收敛精度。

The output characteristic of dye-sensitized solar cell was modeled and simulated by MATLAB software,and the object function based on equivalent circuit current equation was established.The internal parameters of dye-sensitized solar cell were accurately extracted by adaptive particle swarm optimization algorithm,and the effect of weight factor strategy and population size on the parameters extraction was investigated.The relative error between the extracted values and theoretical values is within1%.The adaptive weight factor strategy can significantly improve the searching efficiency and convergence precision of adaptive particle swarm optimization algorithm.The results of adaptive particle swarm optimization algorithm and Simplex algorithm were compared and observed to be in accord with the theoretical value.The Simplex algorithm shows great dependence on initial values.The adaptive particle swarm optimization algorithm has a higher optimization efficiency and convergence precision.