Photovoltaic cell parameter identification method based on improved particle swarm algorithm

Abstract

The invention discloses a photovoltaic cell parameter identification method based on an improved particle swarm algorithm. The implementation process framework of the method comprises the steps of establishing a recursive least-square model form of a photovoltaic cell and determining a parameter to be identified, initializing the position and speed of a particle swarm, calculating a particle fitness value, an individual extremum and a group extremum, updating the position and speed of particles, adding the individual extremum to a Gauss operator, calculating the fitness value and updating the individual extremum, calculating the distance between each particle and a global extremum, calculating the particle fitness value, conducting individual extremum and group extremum updating and outputting the optimal value of the photovoltaic cell parameter to be determined. The photovoltaic cell parameter identification method based on the improved particle swarm algorithm is used for parameter analysis of a series-parallel connection m*n type photovoltaic module array in a grid-connected photovoltaic power generation system and can be used for identifying an undetermined parameter of an I-V equation of the photovoltaic cell, determining an I-V mathematical model of the photovoltaic cell and analyzing failure activating causes of the photovoltaic cell.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a method for identifying photovoltaic cell parameters in a photovoltaic grid-connected power generation system. Background technique [0002] The utilization of solar energy and the research on the characteristics of photovoltaic cells have become hot spots. With the deepening of research, scholars at home and abroad have proposed different photovoltaic cell models to describe the I-V curve. The I-V curve is a macroscopic expression of the characteristics of photovoltaic cells, and the parameters in it reflect the intrinsic characteristics of the model. By identifying the photovoltaic cell parameters, not only can the I-V equation be determined, and the output power of the photovoltaic array can be predicted by using the obtained I-V equation; and the cause of the photovoltaic cell failure can be further studied by analyzing the changes of these parameters. ...

AI Technical Summary

Problems solved by technology

For example, some scholars proposed to apply the genetic algorithm to the field of photovoltaic cell parameter identification. Under the premise of ensuring the identification accuracy, multiple sets of results obtained after photovoltaic cell parameter identification are converged into a set of parameter values. The advantage is that the error can be reduced by iteration. , so as to obtain the optimal estimated value of the parameters; and using the minimum gradient search method in the traditional genetic algorithm, an improved fusion genetic algorithm is formed, which can improve the accuracy and speed of parameter identification, but it still cannot overcome the genetic algorithm's tendency to fall into precocious defect

For another example, some scholars have introduced the particle swarm optimization algorithm into the field of photovoltaic cell parameter identification, but the traditional particle swarm optimization algorithm completes the extreme value optimization by following the individual extreme value and the group extreme value. Although the operation is simple, as the number of iterations continues to increase, While the population is converging and concentrated, each particle is becoming more and more similar, and may not be able to jump out around the local optimal solution, and the later convergence speed is slow and the convergence accuracy is poor, which is difficult to meet the actual needs

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