Parameter Identification Of Solar Photovoltaic System Based On Intelligent Optimization Algorithm

Solar energy is considered as one of the most promising new energy sources because of its abundant reserves,no pollution and no geographical restrictions.Photovoltaic systems can convert solar energy into electricity.As the main component of photovoltaic power generation system,the establishment of mathematical models for solar cells and inverters and the acquisition of accurate model parameters can provide a basis for the design of fault diagnosis and control unit of photovoltaic power generation system.In order to ensure the safe operation of photovoltaic power generation system,the parameter identification method of photovoltaic cells and inverters based on intelligent optimization algorithm is studied in this paper.In order to identify the parameters of photovoltaic cells accurately,a parameter identification method of photovoltaic cell model based on improved locust optimization algorithm was proposed.Aiming at the problems of fast decline of population diversity,early maturity and slow convergence speed in the optimization process of locust optimization algorithm,the improved locust optimization algorithm uses chaotic sequence to distribute initial values and improve the initial population quality;introduces particle swarm optimization algorithm to update the individual position with the current optimal individual as the goal,and then introduces differential evolution strategy.Through the process of mutation,crossover and selection,the diversity of population is enhanced and the ability of the algorithm to jump out of local optimum is improved.The improved locust optimization algorithm is applied to the parameter identification of polycrystalline silicon solar cells.The identification results show that compared with other swarm intelligence optimization algorithms,improved locust optimization algorithm can identify solar cell parameters faster and better.On the basis of the above identification method,a photovoltaic cell parameter identification method based on Lightning algorithm is proposed,and three kinds of photovoltaic cells(monocrystalline silicon,polycrystalline silicon and amorphous silicon cells)are identified.Lightning algorithm is a new meta-heuristic optimization algorithm,inspired by the formation process of lightning in nature.The algorithm has good local and global search ability,high robustness,and does not need any parameter adjustment.The lightning algorithm is used to identify the parameters of photovolt aic cells under different environmental conditions.The results show that the lightning algorithm can quickly and accurately identify the parameters of photovoltaic cells under different illumination conditions.In order to establish an accurate mathematical model of the inverters,an improved moth flame optimization algorithm is proposed to identify the parameters of the inverters.In view of the slow convergence speed of the moth flame algorithm,the moth moves straight to the optimal individual position in the early stage to speed up the optimization speed of the algorithm;in view of the shortcomings of the moth flame algorithm,the Levy flight is used to enhance the diversity of the population and improve the global search ability of the algorithm in the later stage.The improved moth flame algorithm can accurately identify the parameters of the inverters circuit,and can be applied to parameter-based fault diagnosis,operation state monitoring and predictive maintenance of the inverters.