基于改进灰狼算法的光伏微网光学材料参数识别研究

Research on Parameter Identification of Photovoltaic Microgrid System Based on Improved Grey Wolf Algorithm

光学材料在各个领域都具有广泛的应用,准确识别光学材料的参数对于材料设计和性能优化至关重要。光伏微网系统作为一种新兴的可再生能源供电技术,其系统参数的准确识别对于系统设计和运行具有重要意义。提出了一种基于改进灰狼算法的光伏微网光学材料参数识别方法。首先,结合正交学习方法对传统的灰狼优化算法进行了改进,使用一种局部探索方法来识别光伏电池模型中光学材料的不确定参数,并且修改了矢量参数来促进算法两个阶段阶段之间的可靠平衡。其次,结合改进算法对光伏微网系统中的参数进行逐步迭代优化,以进一步提高参数识别的精度。最后使用所提出的改进灰狼优化算法估算了基于单二极管模型(SDM)、双二极管模型(DDM)的光伏模块模型光学材料不确定参数。实验结果表明,该方法能够有效地识别光伏微网系统中光学材料的关键参数,并且在识别精度和收敛速度方面具有较好的性能。

Optical materials have a wide range of applications in various fields,and accurate identification of optical material parameters is crucial for material design and performance optimization.As an emerging renewable energy power supply technology,the accurate identification of system parameters in photovoltaic microgrid systems is of great significance for system design and operation.This article proposes a parameter identification method for photovoltaic microgrid optical materials based on an improved grey wolf algorithm.Firstly,the traditional Grey Wolf optimization algorithm was improved by combining orthogonal learning methods,using a local exploration method to identify uncertain parameters of optical materials in photovoltaic cell models,and modifies vector parameters to promote a reliable balance between the two stages of the algorithm.Secondly,combined with improved algorithms,the parameters in the photovoltaic microgrid system are gradually iteratively optimized to further improve the accuracy of parameter identification.Finally,the improved Grey Wolf optimization algorithm proposed in this article was used to estimate the optical material uncertainty parameters of photovoltaic module models based on single diode model(SDM)and double diode model(DDM).The experimental results show that this method can effectively identify key parameters of optical materials in photovoltaic microgrid systems,and has good performance in recognition accuracy and convergence speed.