Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems

Kaleem Khodabux; Bhamini S. Bhujun; Rani Chinnappa Naidu; Krishna Busawon

doi:10.1109/EFEA56675.2022.10063743

Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems

Kaleem Khodabux, Bhamini S. Bhujun, Rani Chinnappa Naidu, Krishna Busawon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The output power of a wind turbine is usually found to follow a sigmoid curve as a function of wind speed. As a result, identifying the appropriate sigmoid curve to fit the wind turbine power curve is crucial for maximizing the turbine's performance. In this paper, we analyse the effectiveness of several renowned sigmoid functions to correctly model the power curve of a wind turbine with minimum convergence error and reduced complexity. The Logistic and Hill functions are among the candidates. We show that all of the aforementioned sigmoid functions are best suited for modelling WEC power curves if the sigmoid functions' parameters are appropriately determined and adjusted to account for the delay in the power response and to perform as a cubic function in the wind speed locally. When it comes to interpreting the physical meaning of the underlying parameters, they each have their own advantages and drawbacks. Experimental data from a wind farm in Mauritius is used to validate the model.

Original language	English
Title of host publication	2022 7th International Conference on Environment Friendly Energies and Applications
Subtitle of host publication	Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9798350333213
ISBN (Print)	9798350333220
DOIs	https://doi.org/10.1109/EFEA56675.2022.10063743
Publication status	Published - 16 Dec 2022
Event	7th International Conference on Environment Friendly Energies and Applications, EFEA 2022 - Bagatelle Moka MU, Mauritius Duration: 14 Dec 2022 → 16 Dec 2022

Publication series

Name	Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications
Publisher	IEEE
ISSN (Print)	2641-5925
ISSN (Electronic)	2688-2558

Conference

Conference	7th International Conference on Environment Friendly Energies and Applications, EFEA 2022
Country/Territory	Mauritius
City	Bagatelle Moka MU
Period	14/12/22 → 16/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Hill function
Logistic function
Power curve
Weibull Cumulative Distribution

Access to Document

10.1109/EFEA56675.2022.10063743

Cite this

Khodabux, K., Bhujun, B. S., Naidu, R. C., & Busawon, K. (2022). Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems. In 2022 7th International Conference on Environment Friendly Energies and Applications: Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security (Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications). IEEE. https://doi.org/10.1109/EFEA56675.2022.10063743

Khodabux, Kaleem ; Bhujun, Bhamini S. ; Naidu, Rani Chinnappa et al. / Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems. 2022 7th International Conference on Environment Friendly Energies and Applications: Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security. IEEE, 2022. (Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications).

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title = "Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems",

abstract = "The output power of a wind turbine is usually found to follow a sigmoid curve as a function of wind speed. As a result, identifying the appropriate sigmoid curve to fit the wind turbine power curve is crucial for maximizing the turbine's performance. In this paper, we analyse the effectiveness of several renowned sigmoid functions to correctly model the power curve of a wind turbine with minimum convergence error and reduced complexity. The Logistic and Hill functions are among the candidates. We show that all of the aforementioned sigmoid functions are best suited for modelling WEC power curves if the sigmoid functions' parameters are appropriately determined and adjusted to account for the delay in the power response and to perform as a cubic function in the wind speed locally. When it comes to interpreting the physical meaning of the underlying parameters, they each have their own advantages and drawbacks. Experimental data from a wind farm in Mauritius is used to validate the model.",

keywords = "Hill function, Logistic function, Power curve, Weibull Cumulative Distribution",

author = "Kaleem Khodabux and Bhujun, \{Bhamini S.\} and Naidu, \{Rani Chinnappa\} and Krishna Busawon",

year = "2022",

month = dec,

day = "16",

doi = "10.1109/EFEA56675.2022.10063743",

language = "English",

isbn = "9798350333220",

series = "Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications",

publisher = "IEEE",

booktitle = "2022 7th International Conference on Environment Friendly Energies and Applications",

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note = "7th International Conference on Environment Friendly Energies and Applications, EFEA 2022 ; Conference date: 14-12-2022 Through 16-12-2022",

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Khodabux, K, Bhujun, BS, Naidu, RC & Busawon, K 2022, Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems. in 2022 7th International Conference on Environment Friendly Energies and Applications: Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security. Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications, IEEE, 7th International Conference on Environment Friendly Energies and Applications, EFEA 2022, Bagatelle Moka MU, Mauritius, 14/12/22. https://doi.org/10.1109/EFEA56675.2022.10063743

Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems. / Khodabux, Kaleem; Bhujun, Bhamini S.; Naidu, Rani Chinnappa et al.
2022 7th International Conference on Environment Friendly Energies and Applications: Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security. IEEE, 2022. (Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems

AU - Khodabux, Kaleem

AU - Bhujun, Bhamini S.

AU - Naidu, Rani Chinnappa

AU - Busawon, Krishna

PY - 2022/12/16

Y1 - 2022/12/16

N2 - The output power of a wind turbine is usually found to follow a sigmoid curve as a function of wind speed. As a result, identifying the appropriate sigmoid curve to fit the wind turbine power curve is crucial for maximizing the turbine's performance. In this paper, we analyse the effectiveness of several renowned sigmoid functions to correctly model the power curve of a wind turbine with minimum convergence error and reduced complexity. The Logistic and Hill functions are among the candidates. We show that all of the aforementioned sigmoid functions are best suited for modelling WEC power curves if the sigmoid functions' parameters are appropriately determined and adjusted to account for the delay in the power response and to perform as a cubic function in the wind speed locally. When it comes to interpreting the physical meaning of the underlying parameters, they each have their own advantages and drawbacks. Experimental data from a wind farm in Mauritius is used to validate the model.

AB - The output power of a wind turbine is usually found to follow a sigmoid curve as a function of wind speed. As a result, identifying the appropriate sigmoid curve to fit the wind turbine power curve is crucial for maximizing the turbine's performance. In this paper, we analyse the effectiveness of several renowned sigmoid functions to correctly model the power curve of a wind turbine with minimum convergence error and reduced complexity. The Logistic and Hill functions are among the candidates. We show that all of the aforementioned sigmoid functions are best suited for modelling WEC power curves if the sigmoid functions' parameters are appropriately determined and adjusted to account for the delay in the power response and to perform as a cubic function in the wind speed locally. When it comes to interpreting the physical meaning of the underlying parameters, they each have their own advantages and drawbacks. Experimental data from a wind farm in Mauritius is used to validate the model.

KW - Hill function

KW - Logistic function

KW - Power curve

KW - Weibull Cumulative Distribution

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U2 - 10.1109/EFEA56675.2022.10063743

DO - 10.1109/EFEA56675.2022.10063743

M3 - Conference contribution

AN - SCOPUS:85151269994

SN - 9798350333220

T3 - Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications

BT - 2022 7th International Conference on Environment Friendly Energies and Applications

PB - IEEE

T2 - 7th International Conference on Environment Friendly Energies and Applications, EFEA 2022

Y2 - 14 December 2022 through 16 December 2022

ER -

Khodabux K, Bhujun BS, Naidu RC, Busawon K. Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems. In 2022 7th International Conference on Environment Friendly Energies and Applications: Renewable and Sustainable Energy Systems, Hybrid Transportation Systems, Energy Transition, and Energy Security. IEEE. 2022. (Proceedings of the IEEE International Symposium on Environment Friendly Energies and Applications). doi: 10.1109/EFEA56675.2022.10063743

Mathematical approach to modelling sigmoidal power curves for Wind Energy Conversion Systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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