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Commercial wind turbines modeling using single and composite cumulative probability density functions

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Bibliographic Details
Title: Commercial wind turbines modeling using single and composite cumulative probability density functions
Authors: Othman A. M. Omar, Hamdy M. Ahmed, Reda A. Elbarkouky
Publisher Information: Zenodo
Publication Year: 2021
Collection: Zenodo
Subject Terms: Cumulative probability density functions, Mathematical modelling, Power curves, Wind turbines
Description: 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.
Document Type: article in journal/newspaper
Language: unknown
Relation: https://zenodo.org/records/4629323; oai:zenodo.org:4629323
DOI: 10.11591/ijece.v11i1.pp47-56
Availability: 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
Accession Number: edsbas.FC135690
Database: BASE
Description
Abstract: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.
DOI:10.11591/ijece.v11i1.pp47-56