Abstract

The alignment of the wind turbine yaw to the wind direction is an important topic for wind turbine technology by several points of view. For example, the negative impact on power production of an undesired non-optimal yaw alignment can be impressive. The diagnosis of zero-point shifting of the yaw angle is commonly performed by adopting supplementary measurement sources, as for example, light detection and ranging (LIDAR) anemometers. The drawback is that these measurement campaigns have a certain cost against an uncertain diagnosis outcome. There is therefore an increasing interest from wind turbine practitioners in the formulation of zero-point yaw angle shift diagnosis techniques through the use of nacelle anemometer data. This work is devoted to this task and is organized as a test case discussion: a wind farm featuring six multi-megawatt wind turbines is considered. The study of the power factor Cp as function of the yaw error (estimated through nacelle anemometer data) is addressed. The proposed method has been validated through the detection of a 8 deg zero-point shift of the yaw angle of one wind turbine in the test case wind farm. After the correction of this offset, the performance of the wind turbine of interest is shown to be comparable with the nominal. The results of this work therefore support that an appropriate analysis of nacelle anemometer and operation data can be effective for the diagnosis of zero-point shift of the yaw angle of wind turbines.

References

References
1.
Wan
,
S.
,
Cheng
,
L.
, and
Sheng
,
X.
,
2015
, “
Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model
,”
Energies
,
8
(
7
), pp.
6286
6301
. 10.3390/en8076286
2.
Cortina
,
G.
,
Sharma
,
V.
, and
Calaf
,
M.
,
2017
, “
Investigation of the Incoming Wind Vector for Improved Wind Turbine Yaw-Adjustment Under Different Atmospheric and Wind Farm Conditions
,”
Renew. Energy
,
101
, pp.
376
386
. 10.1016/j.renene.2016.08.011
3.
Fleming
,
P.
,
Scholbrock
,
A.
,
Jehu
,
A.
,
Davoust
,
S.
,
Osler
,
E.
,
Wright
,
A. D.
, and
Clifton
,
A.
,
2014
, “
Field-Test Results Using a Nacelle-Mounted Lidar for Improving Wind Turbine Power Capture by Reducing Yaw Misalignment
,”
J. Phys.: Conf. Ser.
,
524
, p.
012002
.
4.
Kragh
,
K.
,
Hansen
,
M.
, and
Mikkelsen
,
T.
,
2011
, “
Improving Yaw Alignment Using Spinner Based Lidar
,”
49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
,
Orlando, FL
,
Jan. 4–7
, p.
264
.
5.
Mikkelsen
,
T.
,
Angelou
,
N.
,
Hansen
,
K.
,
Sjöholm
,
M.
,
Harris
,
M.
,
Slinger
,
C.
,
Hadley
,
P.
,
Scullion
,
R.
,
Ellis
,
G.
, and
Vives
,
G.
,
2013
, “
A Spinner-Integrated Wind Lidar for Enhanced Wind Turbine Control
,”
Wind Energy
,
16
(
4
), pp.
625
643
. 10.1002/we.1564
6.
Kragh
,
K. A.
, and
Hansen
,
M. H.
,
2014
, “
Load Alleviation of Wind Turbines by Yaw Misalignment
,”
Wind Energy
,
17
(
7
), pp.
971
982
. 10.1002/we.1612
7.
Fleming
,
P. A.
,
Gebraad
,
P. M.
,
Lee
,
S.
,
van Wingerden
,
J. -W.
,
Johnson
,
K.
,
Churchfield
,
M.
,
Michalakes
,
J.
,
Spalart
,
P.
, and
Moriarty
,
P.
,
2014
, “
Evaluating Techniques for Redirecting Turbine Wakes Using Sowfa
,”
Renew. Energy
,
70
, pp.
211
218
. 10.1016/j.renene.2014.02.015
8.
Dijk
,
van
,
van Wingerden
,
M. T.
,
Ashuri
,
J.-W.
,
Li
,
T.
,
Rotea
,
Y.
, and
A.
,
M.
,
2016
, “
Yaw-Misalignment and its Impact on Wind Turbine Loads and Wind Farm Power Output
,”
J. Phys.: Conf. Ser.
,
753
, p.
062013
.
9.
van Dijk
,
M. T.
,
van Wingerden
,
J. -W.
,
Ashuri
,
T.
, and
Li
,
Y.
,
2017
, “
Wind Farm Multi-Objective Wake Redirection for Optimizing Power Production and Loads
,”
Energy
,
121
, pp.
561
569
. 10.1016/j.energy.2017.01.051
10.
Saenz-Aguirre
,
A.
,
Zulueta
,
E.
,
Fernandez-Gamiz
,
U.
,
Lozano
,
J.
, and
Lopez-Guede
,
J. M.
,
2019
, “
Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control
,”
Energies
,
12
(
3
), p.
436
. 10.3390/en12030436
11.
Song
,
D.
,
Yang
,
J.
,
Fan
,
X.
,
Liu
,
Y.
,
Liu
,
A.
,
Chen
,
G.
, and
Joo
,
Y. H.
,
2018
, “
Maximum Power Extraction for Wind Turbines Through a Novel Yaw Control Solution Using Predicted Wind Directions
,”
Energy Conversion Manage.
,
157
,
587
599
. 10.1016/j.enconman.2017.12.019
12.
Rabanal
,
A.
,
Ulazia
,
A.
,
Ibarra-Berastegi
,
G.
,
Sáenz
,
J.
, and
Elosegui
,
U.
,
2019
, “
Midas: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
,”
Energies
,
12
(
1
), p.
28
. 10.3390/en12010028
13.
Astolfi
,
D.
,
Castellani
,
F.
,
Scappaticci
,
L.
, and
Terzi
,
L.
,
2017
, “
Diagnosis of Wind Turbine Misalignment Through SCADA Data
,”
Diagnostyka
,
18
(
1
), pp.
17
24
.
14.
Pei
,
Y.
,
Qian
,
Z.
,
Jing
,
B.
,
Kang
,
D.
, and
Zhang
,
L.
,
2018
, “
Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection
,”
Energies
,
11
(
3
), p.
553
. 10.3390/en11030553
15.
IEC
,
2005
,
Power Performance Measurements of Electricity Producing Wind Turbines
, Technical Report No. 61400–12,
International Electrotechnical Commission
,
Geneva, Switzerland
.
16.
Zalkind
,
D. S.
, and
Pao
,
L. Y.
,
2016
, “
The Fatigue Loading Effects of Yaw Control for Wind Plants
,”
American Control Conference (ACC)
,
Boston, MA
,
July 6–8
,
IEEE
,
New York
, pp.
537
542
.
You do not currently have access to this content.