An efficient data sheet based parameter estimation technique of solar PV

© 2024. The Author(s)..

This work develops an efficient parameter estimation technique, based on manufacturer datasheet, to obtain unknown parameter of solar photovoltaic (PV), precisely. Firstly, a nonlinear least square objective function, in terms of variables given in manufacturer datasheet, has been developed. Then, two optimization techniques, namely the Particle Swarn Optimization (PSO) and Harmony Search (HS) are applied on the developed objective function to achieve the optimized result. Further, the correctness of the developed technique is tested by estimating the performance indices, namely percentage maximum power deviation index (%MPDI) and overall model deviation index (OMDI), of two different solar PV, viz., Kyocera KD210GH-2PU (poly-crystalline), and Shell SQ85 (mono-crystalline). It is shown that developed method with PSO outperforms the HS. The developed method with PSO gives the values of %MPDI and OMDI of 0.0214% and 0.213, only. Also, the existing methods, based on hybrid, multi-objective function, numerical method, have been considered for the comparative analysis. It is revealed through the comparative studies that the developed method with PSO has smaller value of MPDI (= 0.0041%) and OMDI (0.005) than the other existing methods. Further, the convergence of the developed method has also been estimated to check the speed of estimation. It is shown that the developed technique converges only in 5 s. In addition, the developed technique avoids the need of extensive data as it is based on manufacturer datasheet.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Scientific reports - 14(2024), 1 vom: 18. März, Seite 6461

Sprache:

Englisch

Beteiligte Personen:

Charu, K M [VerfasserIn]
Thakur, Padmanabh [VerfasserIn]
Rawat, Nikita [VerfasserIn]
Ansari, Fahim [VerfasserIn]
Gupta, Sandeep [VerfasserIn]
Kumar, Mukesh [VerfasserIn]

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Journal Article

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Date Revised 21.03.2024

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1038/s41598-024-57241-5

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM369893913