Parameter Identification Of Photovoltaic Cell Based On Intelligent Optimization Algorithm

As a kind of clean energy,solar energy has the advantages of pollution-free and renewable,so it has attracted much attention in the field of new energy and is considered to be one of the new energy with the most potential for development.Photovoltaic system can convert solar energy into electric energy.Photovoltaic cell is the main component of photovoltaic system.Accurate identification of its parameters is of great significance for photovoltaic system modeling.The parameters with low accuracy will not only cause great errors,but may even lead to the failure of maximum power point tracking.Therefore,the establishment of a mathematical model that can accurately describe the nonlinear characteristics of solar cells and accurately identify its parameters can provide a guarantee for the design and application of solar cell fault diagnosis and maximum power point tracking control technology.It is of practical significance to improve the efficiency of photovoltaic system.In this paper,the parameter identification method of photovoltaic cell based on intelligent optimization algorithm is studied.The specific work is as follows:In order to accurately identify the parameters of photovoltaic cells,a photovoltaic cell parameter identification method based on improved image group nomadic optimization algorithm is proposed.In view of the shortcomings of the image group nomadic optimization algorithm,such as low precision,slow convergence speed and easy to fall into local optimization,chaos initialization is introduced to improve the quality of the initial population,enhance the ergodicity of the population,and add a fast moving operator.The convergence speed and global search ability of the algorithm are greatly improved.The elite strategy is introduced,and the best individual is used to replace the worst individual,so as to speed up the optimization speed of the algorithm and shorten the optimization time.When applied to the parameter identification of solar cell model,the identification result of the improved image group nomadic optimization algorithm is faster and better than that of other algorithms.The parameters of the photovoltaic cell model under different light conditions are identified,and the identification results are in good agreement with the measured data,which shows that the improved image group nomadic optimization algorithm can accurately and effectively identify the parameters of the solar cell model in different environments.In order to facilitate the application of parameter identification in practical engineering,a parameter identification method of photovoltaic cell engineering model based on improved harris hawks optimization algorithm is proposed.In order to solve the problems of harris hawks optimization algorithm,such as the adjustment of the search process is not flexible enough,it can not be targeted to carry out periodic search,and sometimes it will fall into the local optimization algorithm with relatively poor search accuracy,two improvements are made to harris hawks optimization algorithm.The flexible decline strategy is introduced to expand the global search scope at the beginning of the iteration and to extend the local search time at the end of the iteration,which strengthens the global search ability at the initial stage and the local search ability at the later stage.The introduction of golden sine method not only increases the diversity of the population,reduces the possibility of the algorithm falling into local optimization,but also reduces the search space and improves the efficiency of optimization.When applied to the parameter identification of photovoltaic cell engineering model,the identification results obtained by the improved harris hawks optimization algorithm are more accurate than those obtained by other algorithms,and the identification results are more consistent with the measured data.The results show that the improved harris hawks optimization algorithm can accurately identify the parameters of the engineering model of photovoltaic cells in different environments.