Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control

Part I: Clustering based Wind Turbine Model Linearization

Haoran Zhao, Qiuwei Wu, Qinglai Guo, Hongbin Sun, Yusheng Xue

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.
Original languageEnglish
JournalIEEE Transactions on Sustainable Energy
Volume6
Issue number3
Pages (from-to)831-839
Number of pages10
ISSN1949-3029
DOIs
Publication statusPublished - 2015

Bibliographical note

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Keywords

  • Clustering based identification
  • Model predictive control (MPC)
  • Piece wise affine (PWA) model
  • Wind turbine

Cite this

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title = "Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control: Part I: Clustering based Wind Turbine Model Linearization",
abstract = "This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.",
keywords = "Clustering based identification, Model predictive control (MPC), Piece wise affine (PWA) model, Wind turbine",
author = "Haoran Zhao and Qiuwei Wu and Qinglai Guo and Hongbin Sun and Yusheng Xue",
note = "(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.",
year = "2015",
doi = "10.1109/TSTE.2015.2418282",
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pages = "831--839",
journal = "I E E E Transactions on Sustainable Energy",
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Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control : Part I: Clustering based Wind Turbine Model Linearization. / Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai ; Sun, Hongbin; Xue, Yusheng.

In: IEEE Transactions on Sustainable Energy, Vol. 6, No. 3, 2015, p. 831-839.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control

T2 - Part I: Clustering based Wind Turbine Model Linearization

AU - Zhao, Haoran

AU - Wu, Qiuwei

AU - Guo, Qinglai

AU - Sun, Hongbin

AU - Xue, Yusheng

N1 - (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2015

Y1 - 2015

N2 - This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.

AB - This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.

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