用IRAGA与ACA融合算法拟合太阳电池I-V曲线

CORRELATING I-V CURVE OF SOLAR CELLS WITH COMBINATION ALGORITHM OF IRAGA AND ACA

为了较精确获取太阳电池的特性参数,分析比较双指数算法、泰勒比较算法、伪蒙特卡罗等算法拟合太阳电池I-V曲线的不足之处,结合改进加速遗传算法(IRAGA)与蚁群算法(ACA)的优点,提出基于IRAGA与ACA融合算法。首先,以太阳电池模型方程为基础,建立目标函数,确定特性参数的寻优范围;然后制定IRAGA与ACA融合算法的规则,确定信息度初值等相关参数,最后用Matlab进行拟合分析。结果表明:IRAGA与ACA算法的融合,不仅大大提高了原来算法的计算效率,而且加快了算法的收敛速度,避免了产生局部解的概率。而且可准确拟合太阳电池I-V曲线,使得拟合的最大相对偏差可控制在0.1%之内。同时,还可获得特性参数的最佳参数值。

In order to obtain more accurate parameters of solar cells, a fusion algorithm was proposed based on improved accelerating genetic algorithm （IRAGA） and ant colony algorithm （ACA） after analyzing the disadvantages of double exponential algorithms, Taylor comparison algorithm and Quasi-Monte Carlo algorithm in correlating I-V curve of solar cell and combining with the advantages of IRAGA and ACA. First, the objective function was established to determine the scope of optimization parameters based on the solar cell model equation. Then the rules of the fusion algorithm based on IRAGA and ACA was made to determine the degree of initial information and other related parameters. Finally, the match was made with Matlab. The results showed that the integration of IRAGA and ACA algorithm not only greatly improves the computational efficiency of original algorithm, speeds up the convergence rate and avoids the probability of obtaining local solutions, but also the maximum relative deviation of correlated I-V curve of solar cell can be controlled within 0. 1% and the optimal characteristic parameters values can be obtained.