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Journal of Computational Electronics

Erschienen in:

28.08.2022

Parameter estimation of solar PV models with quantum-based avian navigation optimizer and Newton–Raphson method

verfasst von: Tummala S. L. V. Ayyarao

Erschienen in: Journal of Computational Electronics | Ausgabe 6/2022

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Abstract

A mathematical model with precise parameters is required to analyze the performance of a solar photovoltaic generating system. This technical note presents a unique scheme for accurately estimating the parameters of a solar PV system. The proposed method is a combination of quantum-based avian navigation optimizer (QANO) and Newton–Raphson (NR) method. QANO algorithm, a novel metaheuristic algorithm, is employed for identifying a global optimum solution with optimal parameters which suit well the given experimental solar cell/module. The NR method, on the other hand, is used to solve nonlinear equations during the objective function calculation process. Most of the algorithms estimate the parameters based on the conventional objective function, which do not consider the nonlinearities of the I–V characteristics. Such inaccurate models may not be reliable for real-time applications. In this work, an objective function is formulated which offers a more accurate parameters of the equivalent PV models without neglecting nonlinearities. The proposed method is applied to estimate parameters for a single diode model (SDM), a double diode model (DDM), and a PV module. The efficacy of the proposed QANO algorithm is compared to the results of other state-of-the-art algorithms reported in the literature. The proposed algorithm achieves an RMSE of 7.7300630E−04 for SDM and 7.5248E−04 for DDM, which are lower than most of the existing algorithms.

Vorheriger Artikel A modeling study on utilizing In2S3 as a buffer layer in CIGS-based solar cells

Nächster Artikel Photonic bandgap fiber-based gas sensor with high sensitivity and high birefringence

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Titel: Parameter estimation of solar PV models with quantum-based avian navigation optimizer and Newton–Raphson method
verfasst von: Tummala S. L. V. Ayyarao
Publikationsdatum: 28.08.2022
Verlag: Springer US
Erschienen in: Journal of Computational Electronics / Ausgabe 6/2022
Print ISSN: 1569-8025
Elektronische ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-022-01931-8

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