基于改进复数编码共生生物搜索算法的光伏模型参数辨识

Parameter identification of photovoltaic models based on improved complex valued encoding SOS algorithm

中文关键词: 光伏模型  参数辨识  复数编码  改进型共生生物搜索算法

英文关键词: photovoltaic models  parameter identification  complex valued encoding  improved symbiotic organisms search algorithm

中文摘要:

      由于光伏模型具有多模态和非线性的特点,其参数辨识是一个具有挑战性的问题。鉴于传统算法在光伏模型参数辨识领域所面临的可靠性不足、精度低下、易于陷入局部最优解及早熟收敛的局限,提出了一种改进型复数编码共生生物搜索算法（improved complex valued encoding symbiotic organisms search,ICSOS）用于光伏模型参数辨识。为增强传统共生生物搜索算法的寻优能力,引入了复数编码机制,由原先的一维实数编码拓展至二维复数编码空间,以扩大群体的搜索范围,增强算法的寻优能力和速度。仿真验证表明所提出的改进算法在单二极管模型和光伏组件模型参数辨识过程中均具有良好的适用性,与其他优化算法相比,ICSOS算法能够获得更低的均方根误差（root mean square error,RMSE）,且能够快速寻优,有效减少预测误差,提高参数识别的精度。

英文摘要:

      Due to the multimodal and nonlinear nature of photovoltaic（PV）models, parameter identification is a challenging problem. In view of the limitations faced by traditional algorithms in the field of PV model parameter identification, such as insufficient reliability, low accuracy, easy to fall into local optimal solutions and premature convergence, a improved complex valued encoding symbiotic organisms search（ICSOS）is proposed for PV model parameter identification. In order to enhance the optimization ability of the traditional symbiotic organism search algorithm, a complex valued encoding is introduced, which expands the original one-dimensional real number coding to a two-dimensional complex coding space, in order to expand the search range of the population and enhance the optimization ability and speed of the algorithm. Simulation validation shows that the proposed improved algorithm has good applicability in the process of parameter identification in single diode model, and PV module model, and compared with other optimization algorithms, the ICSOS algorithm is able to obtain lower root mean square error（RMSE）values and can quickly find the optimum to effectively reduce the prediction error and improve the accuracy of parameter identification.