Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm
This article is published by AJRSP 2021 and is also available at 2706-6495
Saved in:
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
AJRSP
2025
|
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613134032732160 |
|---|---|
| access_status_str | Open Access |
| author | Reindorf, Nartey Borkor Abubakar, Ali |
| author2 | 0000-0002-5721-4638 |
| author_browse | 0000-0002-5721-4638 Abubakar, Ali Reindorf, Nartey Borkor |
| author_facet | 0000-0002-5721-4638 Reindorf, Nartey Borkor Abubakar, Ali |
| author_sort | Reindorf, Nartey Borkor |
| collection | Thesis |
| description | This article is published by AJRSP 2021 and is also available at 2706-6495 |
| format | Article |
| id | oai:ir.knust.edu.gh:123456789/16068 |
| institution | KNUST (Ghana) |
| language | English |
| last_indexed | 2026-06-10T12:31:18.486Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from KNUSTSpace — Kwame Nkrumah University of Science & Technology (Ghana) |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | AJRSP |
| publisherStr | AJRSP |
| record_format | dspace |
| source_str | KNUSTSpace — Kwame Nkrumah University of Science & Technology (Ghana) |
| spelling | oai:ir.knust.edu.gh:123456789/16068 Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm Reindorf, Nartey Borkor Abubakar, Ali 0000-0002-5721-4638 This article is published by AJRSP 2021 and is also available at 2706-6495 Avoiding over-dependency on the oil-fired energy supply systems motivates many countries to integrate renewable energy into the existing energy supply systems. Solar Photovoltaic technology forms the most promising option for developing such a costeffective and sustainable energy supply system. Generally, the current-voltage curve is used in the performance assessment and analysis of the Photovoltaic module. The accuracy of the equations for the curve depends on accurate cell parameters. However, the extraction of these parameters remains a complex stochastic nonlinear optimization problem. Many studies have been carried out to deal with such problem but still more researches need to be carried out to achieve a minimum error and a high accuracy. The existing researches ignored the variation in the meteorological data though it has a significant impact on the problem design. In this study, the Sample Average Approximation was employed to deal with the uncertainty and the hybrid optimization method was used to get the optimal parameters. The results showed that the Hybrid PSO-GWO produced the most optimal solution: Series resistance(1.4623), Shunt resistance (215.0000), Ideal diode factors (n1 = 0.9500, n2 = 1.6500) with a maximum PV power of 59.850W. The methodology produced realistic results since the variability is dealt with and the Hybrid PSO-GWO finds the optimal solution at a higher convergence rate. KNUST 2025-01-14T09:52:54Z 2025-01-14T09:52:54Z 2021-08-05 Article Academic Journal of Research and Scientific Publishing | Vol 3 | Issue 28 2706-6495 https://ir.knust.edu.gh/handle/123456789/16068 en application/pdf AJRSP |
| spellingShingle | Reindorf, Nartey Borkor Abubakar, Ali Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title | Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title_full | Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title_fullStr | Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title_full_unstemmed | Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title_short | Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm |
| title_sort | optimal extraction of photovoltaic cell parameters for the maximization of photovoltaic power output using a hybrid particle swarm grey wolf optimization algorithm |
| url | https://ir.knust.edu.gh/handle/123456789/16068 |
| work_keys_str_mv | AT reindorfnarteyborkor optimalextractionofphotovoltaiccellparametersforthemaximizationofphotovoltaicpoweroutputusingahybridparticleswarmgreywolfoptimizationalgorithm AT abubakarali optimalextractionofphotovoltaiccellparametersforthemaximizationofphotovoltaicpoweroutputusingahybridparticleswarmgreywolfoptimizationalgorithm |