Parameter Identification of Photovoltaic Cell Model Based on Perturbation and Observation and Modified Gauss-Newton Method

摘要

In order to solve the problem of low parameter identification precision in photovoltaic parameter identification, a photovoltaic cell parameter identification algorithm (PGN) based on Perturbation and Observation and modified Gauss-Newton method is proposed based on the single diode model of photovoltaic cells. The algorithm uses the extreme conditions at the maximum power point to change the original nonlinear equations into an augmented equation set, reducing the number of test points and reducing the computational complexity. And on the basis of the Gauss-Newton method, by adding one dimension search condition, the probability of the target value rising with the iteration is greatly reduced. Experiment and analysis shows that: The accuracy of the proposed algorithm is significantly higher than that of particle swarm optimization, pattern search algorithm, simulated annealing algorithm, genetic algorithm and Gauss-Newton algorithm. It provides a new method for photovoltaic cell performance parameter detection.