光伏组件内部参数辨识与输出特性研究

摘要

基于光伏组件的物理模型快速准确地识别其内部参数对于预测光伏阵列的输出特性、跟踪最大功率点和电池故障模型的特性是非常重要的.而传统数学解析的参数辨识方法存在着辨识参数不准确,一般的智能优化算法精度都优于数学解析法,但现有的粒子群参数辨识方法存在着易陷入早熟和迭代次数过多等问题.对此,提出了一种改进量子粒子群算法,对光伏组件内部5参数进行准确辨识,并对其外部输出特性进行预测.通过MATLAB仿真算例和实际测试数据对该方法进行验证,证明其准确性和适用性.%The rapid and accurate identification of its internal parameters for the prediction of the output characteristics of photovoltaic arrays, tracking the maximum power point and the characteristics of the battery failure model are very important. However, the accuracy of the algorithm is better than that of the mathematical method, but the existing particle swarm parameter identification method has many problems such as premature and premature number of iterations. In order to solve these problems, an improved quantum particle swarm optimization(PSO) algorithm is proposed to accurately identify the five parameters within the PV module and to predict the external output characteristics. The method is validated by MATLAB simulation and practical test data to prove its accuracy and applicability.