Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control: Part II: Implementation with Clustering based
Piece-Wise Affine Wind Turbine Model.

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

doi:10.1109/TSTE.2015.2418281

Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control: Part II: Implementation with Clustering based Piece-Wise Affine Wind Turbine Model.

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

Tsinghua University
State Grid Electric Power System Research Institute

Research output: Contribution to journal › Journal article › Research › peer-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 language	English
Journal	IEEE Transactions on Sustainable Energy
Volume	6
Issue number	3
Pages (from-to)	840-849
Number of pages	10
ISSN	1949-3029
DOIs	https://doi.org/10.1109/TSTE.2015.2418281
Publication status	Published - 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

Dual decomposition
Distributed model predictive control (D-MPC)
Fast gradient method
Wind farm control

Access to Document

10.1109/TSTE.2015.2418281

DMPC Part 2 20150226 hzhao qw finalAccepted author manuscript, 452 KB

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@article{84799f0bda0a44b5b49adf0d1e241c4c,

title = "Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control: Part II: Implementation with Clustering based Piece-Wise Affine Wind Turbine Model.",

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 = "Dual decomposition, Distributed model predictive control (D-MPC), Fast gradient method, Wind farm control",

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.2015.2418281",

language = "English",

volume = "6",

pages = "840--849",

journal = "IEEE Transactions on Sustainable Energy",

issn = "1949-3029",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3",

}

Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control: Part II: Implementation with Clustering based Piece-Wise Affine Wind Turbine Model. / Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai et al.
In: IEEE Transactions on Sustainable Energy, Vol. 6, No. 3, 2015, p. 840-849.

Research output: Contribution to journal › Journal article › Research › peer-review

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

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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.

KW - Dual decomposition

KW - Distributed model predictive control (D-MPC)

KW - Fast gradient method

KW - Wind farm control

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DO - 10.1109/TSTE.2015.2418281

M3 - Journal article

SN - 1949-3029

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JO - IEEE Transactions on Sustainable Energy

JF - IEEE Transactions on Sustainable Energy

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