Research Papers

Optimal Pitch Control Design With Disturbance Rejection for the Controls Advanced Research Turbine

[+] Author and Article Information
David Wenzhong Gao, Tianqi Gao, Margareta Stefanovic

Daniel Felix Ritchie School of
Engineering & Computer Science,
University of Denver,
Denver, CO 80208

Xiao Wang

Daniel Felix Ritchie School of
Engineering & Computer Science,
University of Denver,
Denver, CO 80208;
College of Information Science and Engineering,
Northeastern University,
Shenyang 110819, China
e-mail: wangxiao.owl@gmail.com

Jianhui Wang

College of Information Science and Engineering,
Northeastern University,
Shenyang 110819, China

Xiangjun Li

State Key Laboratory of Control and
Operation of Renewable Energy
and Storage Systems,
China Electric Power Research Institute,
Beijing 100085, China

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received December 23, 2017; final manuscript received July 25, 2018; published online August 31, 2018. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 141(1), 011005 (Aug 31, 2018) (10 pages) Paper No: SOL-17-1505; doi: 10.1115/1.4041097 History: Received December 23, 2017; Revised July 25, 2018

Advanced and model-based control techniques have become prevalent in modern wind turbine controls in the past decade. These methods are more attractive compared to the commonly used proportional-integral-derivative (PID) controller, as the turbine structural flexibility is increased with multiple and coupled modes. The disturbance accommodating control (DAC) is an effective turbine control approach for the above-rated wind speed region. DAC augments the turbine state-space model with a predefined disturbance waveform model, based on which the controller reduces the impact of wind disturbances on the system output (e.g., rotor speed). However, DAC cannot completely reject the wind disturbance in certain situations, and this results in steady-state regulation errors in the turbine rotor speed and electric power. In this paper, we propose a novel wind turbine pitch control using optimal control theory. The obtained feedback and feedforward control terms function to stabilize the turbine system and reject wind disturbances, respectively, derived systematically based on the Hamilton–Jacobi–Bellman (HJB) equation. Simulation results show that the proposed method achieves desired rotor speed regulation with significantly reduced steady-state errors under turbulent winds, which is simulated on the model of the three-bladed controls advanced research turbine (CART3) using the FAST code.

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Fig. 3

The physical configuration of the wind turbine and DOFs including the blades flapwise deflection, shaft torsion and generator rotation

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Fig. 2

CART3 aerodynamic parameters and control regions: (a) CART3 power coefficient Cp surface and (b) CART3 variable-speed operating regions

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Fig. 1

CART3 at the NWTC. (Photo by Dennis Schroeder, NREL 39197)

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Fig. 4

The estimated wind speed given by the observer

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Fig. 5

Diagram of the simulation platform used for CART3 advanced control designs

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Fig. 6

Wind speed profiles for the case study: (a) step-changes wind speed profile in scenario 1, (b) turbulent wind speed profiles in scenario 2, 3, 4, and (c) turbulent wind speed profile in scenario 5

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Fig. 7

System states under step-changed wind speed (20 s ∼ 30 s: 18 m/s, 30 s ∼ 40 s: 17 m/s, 40 s ∼ 50 s: 16 m/s)

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Fig. 8

A comparison of the static feedback- to the time-varying feedback controls: (a) system response and (b) evolution of the matrix P

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Fig. 9

Simulation results obtained under step-changes wind conditions in scenario 1. (a) HSS speed (rpm), (b) pitch angle (deg), (c) rotor power (kW), (d) zoomed pitch angle. (red solid line: the proposed optimal control method, blue dotted line: DAC)

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Fig. 10

Simulation results obtained under turbulent wind conditions in scenario 2, 3, 4. (a), (b) HSS speed and pitch angle in scenario 2, (c), (d) HSS speed and pitch angle in scenario 3, (e), (f) HSS speed and pitch angle in scenario 4. (red solid line: the proposed optimal control method, blue dotted line: DAC)

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Fig. 11

Simulation results in scenario 5



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