摘要
为解决太阳电池双二极管模型中参数辨识准确度低的问题,基于烟花算法提出一种新型的参数辨识方法。该方法模拟烟花的爆炸过程,在条件范围内进行局部寻优,选取一定数量的最优火花进行下一轮迭代,火花将逐渐搜索整个解空间直到找到满足条件的最优解。测试双晶硅太阳电池KC200GT,采用烟花算法获取参数的平均绝对误差为0.0152,遗传算法和粒子群算法的平均绝对误差分别为0.3548、0.2374。实验结果表明:提出的基于烟花算法的太阳电池参数辨识精度明显优于遗传算法及粒子群算法,可为太阳电池的参数辨识提供一种新方法。
In order to improve the accuracy of parameter identification of photo,voltaic cell four,parameter double,diode model,a new parameter identification method is proposed based on fireworks algorithm in this paper.The algorithm simulates the fireworks explosion process and makes a partial optimization in the condition range.The optimal sparks are selected to complete the next iteration,which will gradually search the entire solution space until the optimal solution is found to meet the conditions.Using dual,crystal silicon KC200GT solar cells test data as verification,the average absolute error of the fireworks algorithm is only 0.0152,and it is 0.3548 and 0.2374 when genetic algorithm and particle swarm algorithm is used respectively.The simulation results and the data analysis verifies the feasibility of the fireworks algorithm and show that the fireworks algorithm is obviously superior to the genetic algorithm and the particle swarm algorithm,which provides a new idea for the parameter identification of photo,voltaic cells.
作者
简献忠
郝辽
Jian Xianzhong;Hao Liao(College of Electrical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2020年第4期20-25,共6页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(41075019)。
关键词
太阳电池
参数辨识
局部寻优
双二极管模型
烟花算法
solar cell
parameter identification
local search
double-diode model
fireworks algorithm
