Time-varying unknown wind disturbances influence significantly the dynamics of wind turbines. In this research, we formulate a disturbance observer (DOB) structure that is added to a proportional-integral-derivative (PID) feedback controller, aiming at asymptotically rejecting disturbances to wind turbines at above-rated wind speeds. Specifically, our objective is to maintain a constant output power and achieve better generator speed regulation when a wind turbine is operated under time-varying and turbulent wind conditions. The fundamental idea of DOB control is to conduct internal model-based observation and cancelation of disturbances directly using an inner feedback control loop. While the outer-loop PID controller provides the basic capability of suppressing disturbance effects with guaranteed stability, the inner-loop disturbance observer is designed to yield further disturbance rejection in the low frequency region. The DOB controller can be built as an on–off loop, that is, independent of the original control loop, which makes it easy to be implemented and validated in existing wind turbines. The proposed algorithm is applied to both linearized and nonlinear National Renewable Energy Laboratory (NREL) offshore 5-MW baseline wind turbine models. In order to deal with the mismatch between the linearized model and the nonlinear turbine, an extra compensator is proposed to enhance the robustness of augmented controller. The application of the augmented DOB pitch controller demonstrates enhanced power and speed regulations in the above-rated region for both linearized and nonlinear plant models.

References

References
1.
Jonkman
,
J. M.
,
Butterfield
,
S.
,
Musial
,
W.
, and
Scott
,
G.
,
2009
, “
Definition of a 5-MW Reference Wind Turbine for Offshore System Development
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/TP-500-38060
.
2.
Wright
,
A. D.
,
2004
, “
Modern Control Design for Flexible Wind Turbines
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/TP-500-35816
.
3.
Wright
,
A. D.
, and
Balas
,
M. J.
,
2003
, “
Design of State-Space-Based Control Algorithms for Wind Turbine Speed Regulation
,”
ASME J. Sol. Energy Eng.
,
125
(
4
), pp.
386
395
.
4.
Frost
,
S. A.
,
Balas
,
M. J.
, and
Wright
,
A. D.
,
2009
, “
Direct Adaptive Control of a Utility-Scale Wind Turbine for Speed Regulation
,”
Int. J. Robust Nonlinear Control
,
19
(
1
), pp.
59
71
.
5.
Wang
,
N.
,
Johnson
,
K. E.
, and
Wright
,
A. D.
,
2012
, “
FX-RLS-Based Feedforward Control for LIDAR-Enabled Wind Turbine Load Mitigation
,”
IEEE Trans. Control Syst. Technol.
,
20
(
5
), pp.
1212
1222
.
6.
Selvam
,
K.
,
Kanev
,
S.
, and
van Wingerden
,
J. W.
,
2009
, “
Feedback–Feedforward Individual Pitch Control for Wind Turbine Load Reduction
,”
Int. J. Robust Nonlinear Control
,
19
(
1
), pp.
72
91
.
7.
Chen
,
X.
, and
Tomizuka
,
M.
,
2015
, “
Overview and New Results in Disturbance Observer Based Adaptive Vibration Rejection With Application to Advanced Manufacturing
,”
Int. J. Adapt. Control Signal Process.
,
29
(
11
), pp.
1459
1474
.
8.
Chen
,
X.
, and
Tomizuka
,
M.
,
2013
, “
Control Methodologies for Precision Positioning Systems
,”
American Control Conference
, Washington, DC, June 17–19, pp.
3704
3711
.
9.
Balas
,
M. J.
,
Magar
,
K. S. T.
, and
Frost
,
S. A.
,
2013
, “
Adaptive Disturbance Tracking Theory With State Estimation and State Feedback for Region II Control of Large Wind Turbines
,”
American Control Conference
, Washington, DC, June 17–19, pp.
2220
2226
.
10.
Kempf
,
C. J.
, and
Kobayashi
,
S.
,
1999
, “
Disturbance Observer and Feedforward Design for a High-Speed Direct-Drive Positioning Table
,”
IEEE Trans. Control Syst. Technol.
,
7
(
5
), pp.
513
526
.
11.
Laks
,
J. H.
,
Dunne
,
F.
, and
Pao
,
L. Y.
,
2010
, “
Feasibility Studies on Disturbance Feedforward Techniques to Improve Load Mitigation Performance
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/SR-5000-48598
.
12.
Jonkman
,
J. M.
, and
Buhl
,
M. L.
, Jr.
,
2005
, “
FAST User's Guide
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/EL-500-38230
.
13.
Francis
,
B. A.
, and
Wonham
,
W. M.
,
1976
, “
The Internal Model Principle of Control Theory
,”
Automatica
,
12
(
5
), pp.
457
465
.
14.
Chen
,
X.
, and
Tomizuka
,
M.
,
2012
, “
A Minimum Parameter Adaptive Approach for Rejecting Multiple Narrow-Band Disturbances With Application to Hard Disk Drives
,”
IEEE Trans. Control Syst. Technol.
,
20
(
2
), pp.
408
415
.
15.
Tomizuka
,
M.
,
1987
, “
Zero Phase Error Tracking Algorithm for Digital Control
,”
ASME J. Dyn. Syst. Meas. Control
,
109
(
1
), pp.
65
68
.
16.
Doyle
,
J. C.
,
Francis
,
B. A.
, and
Tannenbaum
,
A. R.
,
1990
,
Feedback Control Theory
,
Macmillan Publishing Co.
,
New York
.
17.
Moriarty
,
P. J.
, and
Hansen
,
A. C.
,
2005
, “
AeroDyn Theory Manual
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/TP-500-36881
.
18.
Jonkman
,
J. M.
,
2009
, “
TurbSim User's Guide: Version 1.50
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/TP-500-46198
.
19.
Hayman
,
G.
,
2012
, “
MLife Theory Manual Version 1.00
,” National Renewable Energy Laboratory, Golden, CO,
Technical Report No. NREL/TP-XXXXX
.
20.
Dunne
,
F.
,
Pao
,
L. Y.
,
Wright
,
A. D.
,
Jonkman
,
B.
, and
Kelley
,
N.
,
2011
, “
Adding Feedforward Blade Pitch Control to Standard Feedback Controllers for Load Mitigation in Wind Turbines
,”
Mechatronics
,
21
(
4
), pp.
682
690
.
21.
Ma
,
Z.
,
Shaltout
,
M. L.
, and
Chen
,
D.
,
2015
, “
An Adaptive Wind Turbine Controller Considering Both the System Performance and Fatigue Loading
,”
ASME J. Dyn. Syst. Meas. Control
,
137
(
11
), p.
111007
.
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