Perturbed stochastic fractal search for solar PV parameter estimation

Chen, Xu and Yue, Hong and Yu, Kunjie (2019) Perturbed stochastic fractal search for solar PV parameter estimation. Energy, 189. ISSN 1873-6785 (https://doi.org/10.1016/j.energy.2019.116247)

[thumbnail of Chen-etal-Energy-2019-Perturbed-stochastic-fractal-search-for-solar-PV-parameter-estimation]
Preview
 Text. Filename: Chen_etal_Energy_2019_Perturbed_stochastic_fractal_search_for_solar_PV_parameter_estimation.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (5MB)| Preview

Abstract

Following the widespread use of solar energy all over the world, the design of high quality photovoltaic (PV) cells has attracted strong research interests. To properly evaluate, control and optimize solar PV systems, it is crucial to establish a reliable and accurate model, which is a challenging task due to the presence of non-linearity and multi-modality in the PV systems. In this work, a new meta-heuristic algorithm (MHA), called perturbed stochastic fractal search (pSFS), is proposed to estimate the PV parameters in an optimization framework. The novelty lies in two aspects: (i) employ its own searching operators, i.e., diffusion and updating, to achieve a balance between the global exploration and the local exploitation; and (ii) incorporate a chaotic elitist perturbation strategy to improve the searching performance. To examine the effectiveness of pSFS, this method is applied to solve three PV estimation problems for different PV models, including single diode, double diode and PV modules. Experimental results and statistical analysis show that the proposed pSFS has improved estimation accuracy and robustness compared with several other algorithms recently developed.

ORCID iDs

Chen, Xu, Yue, Hong ORCID logoORCID: https://orcid.org/0000-0003-2072-6223 and Yu, Kunjie;
CORE (COnnecting REpositories)