The benefit of wind turbine (WT) can be significantly improved through a well-organized condition-based maintenance strategy. However, such a target has not been fully achieved today. One of the major reasons is lack of an efficient WT condition monitoring system (CMS). The existing WT CMSs often involve high initial capital cost, with complex structure, suffer from inefficient management and show unsatisfactory hardware reliability. So, the operators still have desire for an economical, effective, and reliable CMS for their machines. The work reported in this paper is intended to meet such a demand. Because direct drive permanent magnet (PM) WTs are showing increased market share, but the existing WT CMSs are not designed to deal specifically with this new design, this paper reports on a CM technique dedicated to monitoring the drive train of direct drive WTs. Instead of taking the vibration analysis approach that is being popularly adopted by commercial WT CMSs, a novel CM strategy is researched in this paper by introducing generator electrical signals into WT CM and interpreting them by using a dedicated criterion named instantaneous variance (IV) and Teager–Huang transform (THT), i.e. the generator electrical signals will be evaluated first by using the IV, of which the fault detection capability can be further enhanced with the aid of empirical mode decomposition (EMD). Once an abnormality is detected, then detailed THT analysis of the signal will be conducted for further investigation. The technique has been verified experimentally on a specifically designed WT drive train test rig, on which a PM generator rotates at slow variable speed and is subjected to varying load like a real WT does. Considering the electric subassemblies and rotor blades of direct drive WTs are most vulnerable to damage in practice, rotor unbalance and generator winding faults were emulated on the test rig. Experimental results show that the proposed CM technique is effective in detecting both types of faults occurring in the drive train of direct drive PM WTs. In summary, the proposed CM technique can be identified by (i) the CM is accomplished through analyzing the generator electrical signals without resorting to any other information (e.g. vibro-acoustic). Hence, the data acquisition work will be eased off; (ii) no more transducer other than current and voltage sensors are required. Thus, the cost of the CMS will be significantly reduced; (iii) attributed to the distinguished superiorities of THT to traditional spectral analyses in processing nonlinear signals, the proposed technique is more reliable in interpreting WT CM signals; and (iv) the CM criterion IV has a simple computational algorithm. It is therefore suited to both online and offline WT CM applications.

References

References
1.
Pullen
,
A.
, and
Sawyer
,
S.
, eds.,
2010
, “
Global Wind Report—Annual Market Update 2010
,”
Global Wind Energy Council
,
Brussels, Belgium
.
2.
Milborrow
,
D.
,
2006
, “
Operation and Maintenance Costs Compared and Revealed
,”
Windstats Newsletter
,
19
, pp.
1
3
.
3.
Yang
,
S.
,
Bryant
,
A.
,
Mawby
,
P.
,
Xiang
,
D.
,
Ran
,
L.
, and
Tavner
,
P.
,
2011
, “
An Industry-Based Survey of Reliability in Power Electronic Converters
,”
IEEE Trans. Ind. Appl.
,
47
(
3
), pp.
1441
1451
.10.1109/TIA.2011.2124436
4.
Yang
,
W.
,
Tavner
,
P.
,
Crabtree
,
C. J.
,
Feng
,
Y.
, and
Qiu
,
Y.
,
2012
, “
Wind Turbine Condition Monitoring: Technical and Commercial Challenges
,”
Wind Energy
(in press).10.1002/we.1508
5.
Clark
,
T. J.
,
Bauer
,
R. F.
, and
Rasmussen
,
J. R.
,
2004
, “
Wind Power Comes of Age
,”
Orbit
,
24
, pp.
20
27
.
6.
Trzynadlowski
,
A. M.
,
Legowski
,
S. F.
, and
Ghassemzadeh
,
M.
,
1999
, “
Diagnostics of Mechanical Abnormalities in Induction Motors Using Instantaneous Electric Power
,”
IEEE Trans. Energy Convers.
,
14
(
4
), pp.
1417
1423
.10.1109/60.815083
7.
Jeffries
,
W. Q.
,
Chambers
,
J. A.
,
Infield
,
D. G.
,
Coonick
A. H.
, and
Freris
,
L. L.
,
1996
, “
Condition Monitoring of Wind Turbine Blades Via Electrical Power Output
,”
European Union Wind Energy Conference
,
Göteborg, Sweden
, May 20–24, pp.
1046
1049
.
8.
Amirat
,
Y.
,
Benbouzid
,
M. E. H.
,
Al-Ahmar
,
E.
,
Bensaker
,
B.
, and
Turri
,
S.
,
2009
, “
A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems
,”
Renewable Sustainable Energy Rev.
,
13
(
9
), pp.
2629
2636
.10.1016/j.rser.2009.06.031
9.
Ciang
,
C. C.
,
Lee
,
J. R.
, and
Bang
,
H. J.
,
2008
, “
Structural Health Monitoring for a Wind Turbine System: A Review of Damage Detection Methods
,”
Meas. Sci. Technol.
,
19
, pp.
1
20
.10.1088/0957-0233/19/12/122001
10.
Hameed
,
Z.
,
Hong
,
Y. S.
,
Cho
,
Y. M.
,
Ahn
,
S. H.
, and
Song
,
C. K.
,
2009
, “
Condition Monitoring and Fault Detection of Wind Turbines and Related Algorithms: A Review
,”
Renewable Sustainable Energy Rev.
,
13
(
1
), pp.
1
39
.10.1016/j.rser.2007.05.008
11.
Lu
,
B.
,
Li
,
Y.
,
Wu
,
X.
, and
Yang
,
Z.
,
2009
, “
A Review of Recent Advances in Wind Turbine Condition Monitoring and Fault Diagnosis
,”
IEEE Conference on Power Electronics and Machines in Wind Applications
(
PEMWA'09
),
Lincoln, NE
, June 24–26, pp.
1
7
.10.1109/PEMWA.2009.5208325
12.
Tse
,
P. W.
,
Yang
,
W.
, and
Tam
,
H. Y.
,
2004
, “
Machine Fault Diagnosis Through an Effective Exact Wavelet Analysis
,”
J. Sound Vib.
,
277
(
4-5
), pp.
1005
1024
.10.1016/j.jsv.2003.09.031
13.
Yang
,
W.
,
2007
, “
A Natural Way for Improving the Accuracy of the Continuous Wavelet Transforms
,”
J. Sound Vib.
,
306
(
3-5
), pp.
928
939
.10.1016/j.jsv.2007.07.001
14.
Huang
,
N. E.
,
Shen
,
Z.
,
Long
,
S. R.
,
Wu
,
M. C.
,
Shih
,
H. H.
,
Zheng
,
Q.
,
Yen
,
N.
,
Tung
,
C. C.
, and
Liu
,
H. H.
,
1998
, “
The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis
,”
Proc. R. Soc. London, Ser. A
,
454
, pp.
903
995
.10.1098/rspa.1998.0193
15.
Qi
,
K. Y.
,
He
Z. J.
, and
Zi
,
Y. Y.
,
2007
, “
Cosine Window-Based Boundary Processing Method for EMD and Its Application in Rubbing Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
21
, pp.
2750
2760
.10.1016/j.ymssp.2007.04.007
16.
Peng
,
Z. K.
,
Tse
,
P.
, and
Chu
,
F. L.
,
2005
, “
An Improved Hilbert-Huang Transform and Its Application in Vibration Signal Analysis
,”
J. Sound Vib.
,
286
(
1-2
), pp.
187
205
.10.1016/j.jsv.2004.10.005
17.
Yang
,
W.
,
2008
, “
Interpretation of Mechanical Signals Using an Improved Hilbert-Huang Transform
,”
Mech. Syst. Signal Process.
,
22
(
5
), pp.
1061
1071
.10.1016/j.ymssp.2007.11.024
18.
Cexus
,
J. C.
, and
Boudraa
,
A. O.
,
2004
, “
Teager–Huang Analysis Applied to Sonar Target Recognition
,”
Int. J. Signal Process.
,
1
(
1
), pp.
23
27
. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.1378&rep=rep1&type=pdf
19.
Maragos
,
P.
,
Kaiser
J. F.
, and
Quatieri
,
T. F.
,
1993
, “
On Amplitude, Frequency Demodulation Using Energy Operator
,”
IEEE Trans. Signal Process.
,
41
(
4
), pp.
1532
1550
.10.1109/78.212729
20.
Cheng
,
J. S.
,
Yu
,
D. J.
, and
Yang
,
Y.
,
2007
, “
The Application of Energy Operator Demodulation Approach Based on EMD in Machine Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
21
, pp.
668
677
.10.1016/j.ymssp.2005.09.005
21.
Potamianos
,
A.
, and
Maragos
,
P.
,
1994
, “
A Comparison of the Energy Operator and the Hilbert Transform Approach to Signal and Speech Demodulation
,”
Signal Process.
,
37
(
1
), pp.
95
120
.10.1016/0165-1684(94)90169-4
22.
Faulstich
,
S.
, and
Hahn
,
B.
,
2009
, “
Comparison of Different Wind Turbine Concepts Due to Their Effects on Reliability
,”
UpWind, EU supported project nr. 019945(SES6), deliverable WP7.3.2, public report, Kassel, Germany
.
23.
Tavner
,
P.
,
Spinato
,
F.
,
van Bussel
,
G. J. W.
, and
Koutoulakos
,
E.
,
2008
, “
Reliability of Different Wind Turbine Concepts With Relevance to Offshore Application
,”
Proceedings of European Wind Energy Conference
,
Brussels, Belgium
, March 31–April 3.
24.
Tavner
,
P.
,
Ran
,
L.
,
Penman
,
J.
, and
Sedding
,
H.
,
2008
, “
Condition Monitoring of Rotating Electrical Machines
,” IET, Stevenage, UK.
25.
Yang
,
W.
,
Tavner
,
P.
, and
Wilkinson
,
M. R.
,
2009
, “
Condition Monitoring and Fault Diagnosis of a Wind Turbine Synchronous Generator Drive Train
,”
IET Renewable Power Gener.
,
3
(
1
), pp.
17
26
.10.1049/iet-rpg:20080006
26.
Sarma
,
M. S.
,
1979
,
Synchronous Machines, Their Theory, Reliability, and Excitation Systems
,
Gordon and Breach Science Publisher
,
New York
.
27.
Wenxian Yang, Tavner
,
P.
,
Crabtree
,
C. J.
, and
Wilkinson
,
M.
,
2010
, “
Cost-Effective Condition Monitoring for Wind Turbines
,”
IEEE Trans. Ind. Electron.
,
57
(
1
), pp.
263
271
.10.1109/TIE.2009.2032202
28.
Huang
,
N. E.
,
Shen
,
Z.
, and
Long
,
S. R.
,
1999
, “
A New View of Nonlinear Water Waves: The Hilbert Spectrum
,”
Annu. Rev. Fluid Mech.
,
31
, pp.
417
457
.10.1146/annurev.fluid.31.1.417
29.
Maragos
,
P.
,
Kaiser
,
J. F.
, and
Quatieri
,
T. F.
,
1993
, “
Energy Separation in Signal Modulations With Application to Speech Analysis
,”
IEEE Trans. Signal Process.
,
41
(
10
), pp.
3024
3051
.10.1109/78.277799
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