基于线性优化算法的光伏组件参数辨识

PARAMETER IDENTIFICATION OF PHOTOVOLTAIC MODULES BASED ON LINEAR OPTIMIZATION ALGORITHM

为改善光伏组件单二极管模型参数的辨识精度,利用微分电导信息将光伏组件的伏安特性进行线性化处理,提出一种基于线性最小二乘优化的参数辨识算法。通过Photowatt-PWP 201光伏组件室内测试数据对算法有效性进行验证,结果表明其精度高于现有的典型算法,如特殊函数法、拉普拉斯变换法和教与学元启发式算法。进一步通过aSiMicro03036光伏组件室外连续测试数据对算法可靠性进行验证,结果表明在不同太阳辐照度和温度条件下均能快速而准确地辨识光伏组件参数。

In order to improve the identification accuracy of single-diode model parameters for photovoltaic(PV)modules,a linear least squares algorithm for parameter identification is proposed by the linearization of current-voltage(I-V)characteristics of PV modules using the differential conductance information.The effectiveness of the algorithm is validated through indoor test data of the Photowatt-PWP 201 module,and the result shows that the algorithm has a higher accuracy than the existing typical algorithms such as special function method,Laplace transform method,and teaching and learning meta-heuristic method.Furthermore,the robustness of the algorithm is validated through continuous outdoor test data of the aSiMicro03036 module,and the result indicates that the PV parameters can be identified quickly and accurately under various irradiance and temperature conditions.