Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256779" target="_blank" >RIV/61989100:27240/25:10256779 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/25:10256779

Result on the web

<a href="https://www.nature.com/articles/s41598-025-85115-x" target="_blank" >https://www.nature.com/articles/s41598-025-85115-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-85115-x" target="_blank" >10.1038/s41598-025-85115-x</a>

Result language

angličtina

Original language name

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Original language description

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach&apos;s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France &amp; PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 &amp; STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

33

Pages from-to

1-33

UT code for WoS article

001401672000006

EID of the result in the Scopus database

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