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Current Chinese Engineering Science

Author(s): Jiaying Huang, Wangqiang Niu* and Xiaotong Wang

DOI: 10.2174/2665998002666211206104809

Cite As
Output Power Modeling of Wind Turbine Based on State Curve Analysis

Article ID: e061221198579 Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background: In wind power generation, the power curve can reflect the overall power generation performance of a wind turbine. How to make the power curve have high precision and be easy to interpret is a hot research topic.

Objective: Because the current power curve modeling method is not comprehensive in feature selection, the simplified model and state curve of a wind turbine are introduced to avoid feature selection and make the model interpret easily.

Methods: A power modeling method based on different working conditions is proposed. The wind turbine system is simplified into three physical models of blades, mechanical transmission and generator, and the energy transfer is expressed by mathematical expressions. The operation process of the wind turbine is divided into three phases: Constant Power (CP), Constant Speed (CS), and Maximum Power Point Tracking (MPPT), and the power expression of each phase is given after the analysis of state curves.

Results: The effectiveness of the proposed method is verified by the Supervisory Control and Data Acquisition (SCADA) data of a 2MW wind turbine. The experimental results show that the Mean Absolute Percentage Error (MAPE) index of the proposed power modeling method based on state curve analysis is 11.56%, which indicates that the power prediction result of this method is better than that of the sixth-order polynomial regression method, whose MAPE is 13.88%.

Conclusion: The results show that the proposed method is feasible with high transparency and is interpreted easily.

Keywords: Wind turbine, power curve, state curve analysis, feature selection, operation condition, prediction.

Graphical Abstract

[1]
Y. Li, and H. Han, "The present situation of energy in China and the thinking of its future development", Inner Mongolia Sci. Technol. Economy, vol. 1, pp. 3-4+11, 2014.
[2]
P. Guo, and L. Liu, "Modeling and monitoring of multivariable wind turbine power curve", Power Sys. Technol., vol. 42, no. 10, pp. 3347-3354, 2018.
[3]
Y. Chen, A. Mendoza, and D. Griffith, "Experimental and numerical study of high-order complex curvature mode shape and mode coupling on a three-bladed wind turbine assembly", Mech. Syst. Signal Process., vol. 160, no. 107873, 2021.
[4]
M. Lydia, S.S. Kumar, A.I. Selvakumar, and G.E.P. Kumar, "A comprehensive review on wind turbine power curve modeling techniques", Renew. Sustain. Energy Rev., vol. 30, pp. 452-460, 2014.
[http://dx.doi.org/10.1016/j.rser.2013.10.030]
[5]
International Electrotechnical Commission, Power Performance Measurements of Electricity Producing Wind Turbines, IEC 61400-12-1, Geneva, Switzerland, 2005.
[6]
M. Marciukaitis, I. Zutautaite, L. Martisauskas, B. Joksas, G. Gecevicius, and A. Sfetsos, "Non-linear regression model for wind turbine power curve", Renew. Energy, vol. 113, pp. 732-741, 2017.
[http://dx.doi.org/10.1016/j.renene.2017.06.039]
[7]
Y. Chen, P. Logan, P. Avitabile, and J. Dodson, "Non-model based expansion from limited points to an augmented set of points using Chebyshev polynomials", Exp. Tech., vol. 43, no. 5, pp. 521-543, 2019.
[http://dx.doi.org/10.1007/s40799-018-00300-0]
[8]
Y. Chen, P. Avitabile, C. Page, and J. Dodson, "A polynomial based dynamic expansion and data consistency assessment and modification for cylindrical shell structures", Mech. Syst. Signal Process., vol. 154, no. 107574, 2021.
[9]
A. Marvuglia, and A. Messineo, "Monitoring of wind farms’ power curves using machine learning techniques", Appl. Energy, vol. 98, pp. 574-583, 2012.
[http://dx.doi.org/10.1016/j.apenergy.2012.04.037]
[10]
L.T. Paiva, C.V. Veiga Rodrigues, and J.M.L.M. Palma, "Determining wind turbine power curves based on operating conditions", Wind Energy (Chichester Engl.), vol. 17, no. 10, pp. 1563-1575, 2014.
[http://dx.doi.org/10.1002/we.1651]
[11]
C. Croonenbroeck, and D. Ambach, "A selection of time series models for short-to medium-term wind power forecasting", J. Wind Eng. Ind. Aerodyn., vol. 136, pp. 201-210, 2015.
[http://dx.doi.org/10.1016/j.jweia.2014.11.014]
[12]
C. Croonenbroeck, and C.M. Dahl, "Accurate medium-term wind power forecasting in a censored classification framework", Energy, vol. 73, pp. 221-232, 2014.
[http://dx.doi.org/10.1016/j.energy.2014.06.013]
[13]
J. Xie, and P. Guo, "Deep neural network modeling on power curve based on multi-variable selection", Huadian Technology, vol. 41, no. 8, pp. 27-31-52, 2019.
[14]
F. Pelletier, C. Masson, and A. Tahan, "Wind turbine power curve modelling using artificial neural network", Renew. Energy, vol. 89, pp. 207-214, 2016.
[http://dx.doi.org/10.1016/j.renene.2015.11.065]
[15]
X. Wang, W. Niu, and W. Gu, "Applied systems theory: Wind turbine output power prediction based on wind energy utilization coefficient", Int. J. Circuits Sys. Signal Process., vol. 15, pp. 356-366, 2021.
[http://dx.doi.org/10.46300/9106.2021.15.39]
[16]
Y. Chen, D. Joffre, and P. Avitabile, "Underwater dynamic response at limited points expanded to full-field strain response", J. Vib. Acoust., vol. 140, no. 5, 2018.
[http://dx.doi.org/10.1115/1.4039800]
[17]
F. Xu, F.G. Yuan, L. Liu, J. Hu, and Y. Qiu, "Performance prediction and demonstration of a miniature horizontal axis wind turbine", J. Energy Eng., vol. 139, no. 3, pp. 143-152, 2013.
[http://dx.doi.org/10.1061/(ASCE)EY.1943-7897.0000125]
[18]
S. Heier, Grid integration of wind energy: Onshore and offshore conversion systems., 3rd ed John Wiley & Sons: Chichester, 2014.
[http://dx.doi.org/10.1002/9781118703274]
[19]
W. Chen, M. Jin, J. Huang, Y. Chen, and H. Song, "A method to distinguish harmonic frequencies and remove the harmonic effect in operational modal analysis of rotating structures", Mech. Syst. Signal Process., vol. 161, no. 107928, 2021.
[20]
X. Zhang, Research on the Wind Turbine Drive-train System State Evaluation Based on Operation condition, 2016.
[21]
M. Zeng, B. Tan, F. Ding, B. Zhang, H. Zhou, and Y. Chen, "An experimental investigation of resonance sources and vibration transmission for a pure electric bus", Proc. Inst. Mech. Eng., D J. Automob. Eng., vol. 234, no. 4, pp. 950-962, 2019.
[http://dx.doi.org/10.1177/0954407019879258]
[22]
Q. Sun, C. Liu, and C. Zhen, "Abnormal detection of wind turbine operating conditions based on state curves", J. Energy Eng., vol. 145, no. 5, 2019.
[http://dx.doi.org/10.1061/(ASCE)EY.1943-7897.0000612]
[23]
S. Ma, "Optimization of generator speed controller used for the modern horizontal shaft three blades wind turbines", Power System Protec-tion and Control, vol. 46, no. 5, pp. 129-134, 2018.
[24]
Z. Huo, Wind turbine control technology., China Water Power Press: Beijing, China, 2010.
[25]
J. Zhang, B. Zhang, N. Zhang, C. Wang, and Y. Chen, "A novel robust event-triggered fault tolerant automatic steering control approach of autonomous land vehicles under in-vehicle network delay", Int. J. Robust Nonlinear Control, vol. 31, pp. 1-29, 2021.
[http://dx.doi.org/10.1002/rnc.5393]
[26]
J.G. Slootweg, H. Polinder, and W.L. Kling, "Dynamic modelling of a wind turbine with doubly fed induction generator", Power Engineering Society Summer Meeting, vol. 1, pp. 644-649, 2001.
[http://dx.doi.org/10.1109/PESS.2001.970114]
[27]
S. M. R. Kazmi, H. Goto, H. Guo, and O. Ichinokura, "Review and critical analysis of the research papers published till date on maximum power point tracking in wind energy conversion system", IEEE Energy Convers. Cong. Expos., vol. 1, pp. 4075-4082, 2010.
[http://dx.doi.org/10.1109/ECCE.2010.5617747]
[28]
P. Kundur, Power System stability and control., McGraw-Hill: New York, USA, 1994.
[29]
A. Betz, Wind-Energie und ihre ausnutzung durch Windmühlen., Vandenhoeck und Ruprecht: Göttingen, Germany, 1926.
[30]
D.C. Montgomery, Introduction to statistical quality control., 5th ed John Wiley & Sons: New York, USA, 2005.