A novel monotonic wind turbine power-speed characteristics model

Al-Motasem Aldaoudeyeh; Khaled Alzaareer; Di Wu; Mohammad Obeidat; Salman Harasis; Zeyad Al-Odat; Qusay Salem; Al-Motasem Aldaoudeyeh; Khaled Alzaareer; Di Wu; Mohammad Obeidat; Salman Harasis; Zeyad Al-Odat; Qusay Salem

doi:10.3934/energy.2023056

AIMS Energy

2023, Volume 11, Issue 6: 1231-1251. doi: 10.3934/energy.2023056

Previous Article Next Article

Research article Special Issues

A novel monotonic wind turbine power-speed characteristics model

1.
Department of Electrical Power and Mechatronics Engineering, Tafila Technical University, Tafila, Jordan
2.
Energy Engineering Department, Al Hussein Technical University, Amman, Jordan
3.
North Dakota State University, North Dakota, USA
4.
Department of Computer and Communications Engineering, Tafila Technical University, Tafila, Jordan
5.
Department of Electrical Engineering, Princess Sumaya University for Technology, Amman, Jordan

Received: 26 July 2023 Revised: 13 October 2023 Accepted: 19 October 2023 Published: 23 November 2023

Major issues with logistic functions (LFs) in modeling wind turbine power-speed characteristics (WTPSCs) include: 1. low accuracy near cut-in and rated wind speeds due to lack of continuity; 2. difficulties in fitting their parameters because of ill-conditioning; 3. no guaranteed monotonicity; 4. no systematic way to determine upper and lower limits for their parameters. The literature also reports that six parameter LFs may sometimes provide less accurate results than five, four, and three parameter models, implying: 1. they are unsuitable for WTPSC modeling; 2. lack of systematic method to determine upper and lower limits for optimization algorithms to search in. In this paper, we propose a new six parameter LF then employ subspace trust-region (STIR) algorithm to estimate its parameters. We compare the accuracy of our six parameter model to others from the literature. With $ 42 $ on-shore and off-shore WTs database of ratings varying from 275 to 8000 kW, we the comprehensiveness of our model. The results show an average mean absolute percent error (MAPE) of 2.383 × 10⁻³. Furthermore, our model reduces average and median normalized root mean square error (NRMSE) by $ 32.3\% $ and $ 38.5 \% $, respectively.
- logistic functions,
- wind turbine power curve,
- parametric models,
- parameters estimation,
- subspace trust region
Citation: Al-Motasem Aldaoudeyeh, Khaled Alzaareer, Di Wu, Mohammad Obeidat, Salman Harasis, Zeyad Al-Odat, Qusay Salem. A novel monotonic wind turbine power-speed characteristics model[J]. AIMS Energy, 2023, 11(6): 1231-1251. doi: 10.3934/energy.2023056

Related Papers:

Abstract

Major issues with logistic functions (LFs) in modeling wind turbine power-speed characteristics (WTPSCs) include: 1. low accuracy near cut-in and rated wind speeds due to lack of continuity; 2. difficulties in fitting their parameters because of ill-conditioning; 3. no guaranteed monotonicity; 4. no systematic way to determine upper and lower limits for their parameters. The literature also reports that six parameter LFs may sometimes provide less accurate results than five, four, and three parameter models, implying: 1. they are unsuitable for WTPSC modeling; 2. lack of systematic method to determine upper and lower limits for optimization algorithms to search in. In this paper, we propose a new six parameter LF then employ subspace trust-region (STIR) algorithm to estimate its parameters. We compare the accuracy of our six parameter model to others from the literature. With $ 42 $ on-shore and off-shore WTs database of ratings varying from 275 to 8000 kW, we the comprehensiveness of our model. The results show an average mean absolute percent error (MAPE) of 2.383 × 10⁻³. Furthermore, our model reduces average and median normalized root mean square error (NRMSE) by $ 32.3\% $ and $ 38.5 \% $, respectively.

References

[1]	Global Wind Energy Council (2021) Global wind report 2021, Brussels: Global Wind Energy Council, 6–7.
[2]	Willis DJ, Niezrecki C, Kuchma D, et al. (2018) Wind energy research: State-of-the-art and future research directions. Renewable Energy 125: 133–154. https://doi.org/10.1016/j.renene.2018.02.049 doi: 10.1016/j.renene.2018.02.049
[3]	Hitaj C, Löschel A (2019) The impact of a feed-in tariff on wind power development in germany. Resour Energy Econ 57: 18–35. https://doi.org/10.1016/j.reseneeco.2018.12.001 doi: 10.1016/j.reseneeco.2018.12.001
[4]	Lin BQ, Chen YF (2019) Impacts of policies on innovation in wind power technologies in China. Appl Energy 247: 682–691. https://doi.org/10.1016/j.apenergy.2019.04.044 doi: 10.1016/j.apenergy.2019.04.044
[5]	Al Motasem IA, Alzaareer K (2020) Evaluating the accuracy of wind turbine power-speed characteristics fits for the generator control region. Int J Renewable Energy Res 10: 1031–1041. https://doi.org/10.20508/ijrer.v10i2.10955.g7975 doi: 10.20508/ijrer.v10i2.10955.g7975
[6]	Hu Y, Xi YH, Pan CY, et al. (2020) Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating. Renewable Energy 146: 2095–2111. https://doi.org/10.1016/j.renene.2019.08.043 doi: 10.1016/j.renene.2019.08.043
[7]	Shokrzadeh S, Jozani MJ, Bibeau E (2014) Wind turbine power curve modeling using advanced parametric and nonparametric methods. IEEE Trans Sustainable Energy 5: 1262–1269. https://doi.org/10.1109/TSTE.2014.2345059 doi: 10.1109/TSTE.2014.2345059
[8]	Aldaoudeyeh AM, Alzaareer K, Harasis S, et al. (2021) A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets. IET Renewable Power Gener 16: 287–299. https://doi.org/10.1049/rpg2.12309 doi: 10.1049/rpg2.12309
[9]	Sohoni V, Gupta SC, Nema RK (2016) A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems. J Energy 2016: 1–18. https://doi.org/10.1155/2016/8519785 doi: 10.1155/2016/8519785
[10]	Aldaoudeyeh AMI, Alzaareer K (2020) Statistical analysis of wind power using weibull distribution to maximize energy yield. 2020 IEEE PES/IAS PowerAfrica 2020: 1–5.
[11]	Pei S, Li Y (2019) Wind turbine power curve modeling with a hybrid machine learning technique. Appl Sci 9: 4930. https://doi.org/10.3390/app9224930 doi: 10.3390/app9224930
[12]	Kawano T, Wallbridge N, Plummer C (2020) Logistic models for simulating the growth of plants by defining the maximum plant size as the limit of information flow. Plant Signaling Behav 15: 1709718. https://doi.org/10.1080/15592324.2019.1709718 doi: 10.1080/15592324.2019.1709718
[13]	Rahimi I, Chen F, Gandomi AH (2021) A review on covid-19 forecasting models. Neural Comput Appl 35: 23671–23681. https://doi.org/10.1007/s00521-020-05626-8 doi: 10.1007/s00521-020-05626-8
[14]	Al-Hinai A, Charabi Y, Aghay Kaboli SH (2021) Offshore wind energy resource assessment across the territory of oman: A spatial-temporal data analysis. Sustainability 13: 2862. https://doi.org/10.3390/su13052862 doi: 10.3390/su13052862
[15]	Lydia M, Selvakumar AI, Kumar SS, et al. (2013) Advanced algorithms for wind turbine power curve modeling. IEEE Trans Sustainable Energy 4: 827–835. https://doi.org/10.1109/TSTE.2013.2247641 doi: 10.1109/TSTE.2013.2247641
[16]	Taslimi-Renani E, Modiri-Delshad M, Elias MFM, et al. (2016) Development of an enhanced parametric model for wind turbine power curve. Appl Energy 177: 544–552. https://doi.org/10.1016/j.apenergy.2016.05.124 doi: 10.1016/j.apenergy.2016.05.124
[17]	Zou R, Yang J, Wang Y, et al. (2021) Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer. Appl Energy 304: 117707. https://doi.org/10.1016/j.apenergy.2021.117707 doi: 10.1016/j.apenergy.2021.117707
[18]	Jing B, Qian Z, Zareipour H, et al. (2021) Wind turbine power curve modelling with logistic functions based on quantile regression. Appl Sci 11: 3048. https://doi.org/10.3390/app11073048 doi: 10.3390/app11073048
[19]	Villanueva D, Feijóo A (2018) Comparison of logistic functions for modeling wind turbine power curves. Electr Power Syst Res 155: 281–288. https://doi.org/10.1016/j.epsr.2017.10.028 doi: 10.1016/j.epsr.2017.10.028
[20]	Yan J, Zhang H, Liu Y, et al. (2019) Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling. Appl Energy 239: 1356–1370. https://doi.org/10.1016/j.apenergy.2019.01.180 doi: 10.1016/j.apenergy.2019.01.180
[21]	Villanueva D, Feijóo A (2020) A review on wind turbine deterministic power curve models. Appl Sci 10: 4186. https://doi.org/10.3390/app10124186 doi: 10.3390/app10124186
[22]	Branch MA, Coleman TF, Li Y (1999) A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM J Sci Comput 21: 1–23. https://doi.org/10.1137/S1064827595289108 doi: 10.1137/S1064827595289108
[23]	Conn AR, Gould NIM, Toint PL (1998) Global convergence of a class of trust region algorithms for optimization with simple bounds. SIAM J Numer Anal 25: 433–460. https://doi.org/10.1137/0725029 doi: 10.1137/0725029
[24]	Kamandi A, Amini K, Ahookhosh M (2016) An improved adaptive trust-region algorithm. Optim Lett 11: 555–569. https://doi.org/10.1007/s11590-016-1018-4 doi: 10.1007/s11590-016-1018-4
[25]	Villanueva D, Feijóo AE (2016) Reformulation of parameters of the logistic function applied to power curves of wind turbines. Electr Power Syst Res 137: 51–58. https://doi.org/10.1016/j.epsr.2016.03.045 doi: 10.1016/j.epsr.2016.03.045
[26]	Lapira E, Brisset D, Ardakani HD, et al. (2012) Wind turbine performance assessment using multi-regime modeling approach. Renewable Energy 45: 86–95. https://doi.org/10.1016/j.renene.2012.02.018 doi: 10.1016/j.renene.2012.02.018
[27]	Lin Z, Liu X (2020) Wind power forecasting of an offshore wind turbine based on high-frequency scada data and deep learning neural network. Energy 201: 117693. https://doi.org/10.1016/j.energy.2020.117693 doi: 10.1016/j.energy.2020.117693
[28]	Wang Y, Hu Q, Li L, et al. (2019) Approaches to wind power curve modeling: A review and discussion. Renewable Sustainable Energy Rev 116: 109422. https://doi.org/10.1016/j.rser.2019.109422 doi: 10.1016/j.rser.2019.109422
[29]	International Electrotechnical Commission (2007) Wind energy generation systems–-Part 12-1: Power performance measurements of electricity producing wind turbines. International Electrotechnical Commission (IEC), IEC Central Office, 3: 2017-03.
[30]	Sunderland K, Woolmington T, Blackledge J, et al. (2013) Small wind turbines in turbulent (urban) environments: A consideration of normal and weibull distributions for power prediction. J Wind Eng Ind Aerodyn 121: 70–81. https://doi.org/10.1016/j.jweia.2013.08.001 doi: 10.1016/j.jweia.2013.08.001
[31]	Bilendo F, Meyer A, Badihi H, et al. (2022) Applications and modeling techniques of wind turbine power curve for wind farms–-A review. Energies 16: 180. https://doi.org/10.3390/en16010180 doi: 10.3390/en16010180
[32]	Mehrjoo M, Jozani MJ, Pawlak M (2020) Wind turbine power curve modeling for reliable power prediction using monotonic regression. Renewable Energy 147: 214–222. https://doi.org/10.1016/j.renene.2019.08.060 doi: 10.1016/j.renene.2019.08.060
[33]	Pelletier F, Masson C, Tahan A (2016) Wind turbine power curve modelling using artificial neural network. Renewable Energy 89: 207–214. https://doi.org/10.1016/j.renene.2015.11.065 doi: 10.1016/j.renene.2015.11.065

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)