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Neural Network Based Power Tracking Control of Wind Farm

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

This paper investigates the power tracking control problem of wind farm consisting of a large number of wind turbines, each of which is to deliver certain amount of power so that the combined power from the wind farm is able to meet the total power demand. For such power tracking control problem, the precise total demanded power is unavailable and there involve modeling uncertainties as well as external disturbances. To address the issue of unknown power trajectory, an analytical model is proposed to reconstruct the unknown desired power profile. Neural network based control scheme is developed to ensure stable power tracking.

L. Liang—This work was supported in part by technology transformation program of Chongqing higher education university (KJZH17102).

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References

  1. Meng, W.C., Yang, Q.M., Ying, Y., Sun, Y., Yang, Z.Y., Sun, Y.X.: Adaptive power capture control of variable-speed wind energy conversion systems with guaranteed transient and steady-state performance. IEEE Trans. Energy Convers. 28(3), 716–725 (2013)

    Article  Google Scholar 

  2. Liu, F.: Stabilization of switched linear system with bounded disturbances and unobservable switchings. Sci. China Ser. F Inf. Sci. 50(5), 711–718 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Song, Y.D., Dhinakaran, B., Bao, X.Y.: Variable speed control of wind turbines using nonlinear and adaptive algorithms. J. Wind Eng. Ind. Aerodyn. 85(3), 293–308 (2000)

    Article  Google Scholar 

  4. Beltran, B., Ahmed-Ali, T., Benbouzid, M.: High-order sliding mode control of variable-speed wind turbines. IEEE Trans. Ind. Electron. 56(9), 3314–3321 (2009)

    Article  Google Scholar 

  5. She, Y., She, X., Baran, M.: Universal tracking control of wind conversion system for purpose of maximum power acquisition under hierarchical control structure. IEEE Trans. Energy Convers. 26(3), 766–775 (2011)

    Article  Google Scholar 

  6. Ge, S.S., Lee, T.H., Wang, J.: Adaptive control of non-affine nonlinear systems using neural networks. Proc. IEEE Int. Symp. Intell. Control 180(17), 13–18 (2000)

    Google Scholar 

  7. Battilotti, S., De Santis, A.: Robust output feedback control of nonlinear stochastic systems using neural networks. IEEE Trans. Neural Netw. Learn. Syst. 14(1), 103–116 (2003)

    Article  Google Scholar 

  8. MohammadZadeh, S., Masoumi, A.A.: Modeling residential electricity demand using neural network and econometrics approaches. In: International Conference on CIE, pp. 1–6 (2010)

    Google Scholar 

  9. Song, Y.D., Zhang, B.B., Zhao, K.: Neuroadaptive control of unknown MIMO systems tracking uncertain target under sensor failures. Automation (2016, under review)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Liyuan Liang & Yongduan Song

  2. The Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education and School of Automation, Chongqing University, Chongqing, 400044, China

    Yongduan Song & Mi Tan

Authors
  1. Liyuan Liang
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  2. Yongduan Song
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  3. Mi Tan
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Corresponding author

Correspondence to Yongduan Song .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Liang, L., Song, Y., Tan, M. (2017). Neural Network Based Power Tracking Control of Wind Farm. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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