Algorithms, Vol. 18, Pages 598: Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

Algorithms doi: 10.3390/a18100598

Authors:
Wai Lun Lo
Henry Shu Hung Chung
Richard Tai Chiu Hsung
Hong Fu
Tony Yulin Zhu
Tak Wai Shen
Harris Sik Ho Tsang

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%.

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