Photovoltaic cell parameter identification method based on elite mass differential evolution algorithm

Abstract

The invention discloses a photovoltaic cell parameter identification method based on an elite mass differential evolution algorithm. The implementation process framework comprises establishing a photovoltaic cell model and confirming the parameters required to be identified; collecting the data and setting a target function; carrying out population initialization, calculating a fitness value, and dividing the population into an elitist level and a crowd level, and performing mutation operation and crossover operation on the elitist level and the mass level, judging whether the elitist level and the mass level exceed a boundary, calculating the fitness value, performing selection operation, updating a scaling factor F and a crossover probability factor CR, removing the duplicates, and finally outputting an optimal parameter value needing to be identified in a photovoltaic cell. The method is used for identifying the parameters of the photovoltaic cell with the m * n type photovoltaic array in a photovoltaic grid-connected power generation system, the undetermined parameters of the photovoltaic cell I-V equation can be identified, the output power of the photovoltaic cell can be solved, and the identified parameters are used for analyzing the fault of the photovoltaic cell.

Description

technical field [0001] The invention relates to a photovoltaic cell parameter identification method based on an elite-mass differential evolution algorithm, and belongs to the technical field of new energy sources. Background technique [0002] In recent years, with the development of green economy, the photovoltaic power generation industry has ushered in a huge development. Photovoltaic cells are an important part of photovoltaic power generation systems, but the output characteristics of photovoltaic power generation systems are greatly affected by the output characteristics of photovoltaic cells. By identifying the parameters that reflect the intrinsic characteristics of photovoltaic cells, not only can the I-V equation be determined, so that the output power of the photovoltaic array can be predicted using the obtained I-V equation; and the cause of failure of photovoltaic cells can be further studied and improved by analyzing the changes of these parameters . Therefo...

AI Technical Summary

Problems solved by technology

Some scholars apply genetic algorithm (GA) to photovoltaic cell parameter identification, but this method has a slow convergence speed and is easy to fall into local optimum; some scholars apply particle swarm optimization (PSO) to photovoltaic cell parameter identification, but this method The method is easy to fall into premature maturity and stagnation

Although these methods can be optimized, they often cannot obtain the optimal solution with higher precision because of some defects in the algorithm itself.

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