Commercial wind turbines modeling using single and composite cumulative probability density functions
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| Název: | Commercial wind turbines modeling using single and composite cumulative probability density functions |
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| Autoři: | Othman A. M. Omar, Hamdy M. Ahmed, Reda A. Elbarkouky |
| Informace o vydavateli: | Zenodo |
| Rok vydání: | 2021 |
| Sbírka: | Zenodo |
| Témata: | Cumulative probability density functions, Mathematical modelling, Power curves, Wind turbines |
| Popis: | As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | unknown |
| Relation: | https://zenodo.org/records/4629323; oai:zenodo.org:4629323 |
| DOI: | 10.11591/ijece.v11i1.pp47-56 |
| Dostupnost: | https://doi.org/10.11591/ijece.v11i1.pp47-56 https://zenodo.org/records/4629323 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Přístupové číslo: | edsbas.FC135690 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results. |
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| DOI: | 10.11591/ijece.v11i1.pp47-56 |