The operational reliability of wind energy conversion systems (WECSs) has attracted a lot of attention recently. This paper is concerned with sensor fault detection (FD) and isolation problems for variable-speed WECSs by using a novel filtering method. A physical model of WECS with typical sensor faults is first built. Due to the non-Gaussianity of both wind speed and measurement noises in WECSs, an improved entropy optimization criterion is then established to design the filter for WECSs. Different from previous entropy-filtering results, the generalized density evolution equation (GDEE) is adopted to reveal the relationship among the estimation error, non-Gaussian noises, and the filter gain. The sensors FD and isolation algorithms are then obtained by evaluating the decision rule based on the residual signals generated by the filter. Finally, simulation results show that the sensor faults in WECSs can be detected and isolated effectively by using the proposed method.

References

References
1.
Amirat
,
Y.
,
Benbouzid
,
M. E. H.
,
Al-Ahmara
,
E.
,
Bensakerb
,
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
.
2.
Spinato
,
F.
,
Tavner
,
P. J.
,
van Bussel
,
G. J. W.
, and
Koutoulakos
,
E.
,
2009
, “
Reliability of Wind Turbine Subassemblies
,”
IET Renewable Power Gener.
,
3
(
4
), pp.
387
401
.
3.
Frank
,
P. M.
, and
Ding
,
S. X.
,
1997
, “
Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems
,”
J. Process Control
,
7
(
6
), pp.
403
424
.
4.
Basseville
,
M.
, and
Nikiforov
,
I.
,
2002
, “
Fault Isolation for Diagnosis: Nuisance Rejection and Multiple Hypothesis Testing
,”
Annu. Rev. Control
,
26
(
2
), pp.
189
202
.
5.
Van Eykeren
,
L.
, and
Chu
,
Q. P.
,
2014
, “
Sensor Fault Detection and Isolation for Aircraft Control Systems by Kinematic Relations
,”
Control Eng. Pract.
,
31
, pp.
200
210
.
6.
Rahme
,
S.
, and
Meskin
,
N.
,
2015
, “
Adaptive Sliding Mode Observer for Sensor Fault Diagnosis of an Industrial Gas Turbine
,”
Control Eng. Pract
,
38
, pp.
57
74
.
7.
Carminati
,
M.
,
Ferrari
,
G.
,
Grassetti
,
R.
, and
Sampietro
,
M.
,
2012
, “
Real-Time Data Fusion and MEMS Sensors Fault Detection in an Aircraft Emergency Attitude Unit Based on Kalman Filtering
,”
IEEE Sens. J.
,
12
(
10
), pp.
2984
2992
.
8.
Liu
,
J.
,
Xu
,
D.
, and
Yang
,
X.
,
2008
, “
Sensor Fault Detection Invariable Speed Wind Turbine System Using H/H Method
,”
7th World Congress on Intelligent Control and Automation
, WCICA
2008
, pp.
4265
4269
.
9.
Wei
,
X.
, and
Liu
,
L.
,
2010
, “
Fault Detection of Large Scale Wind Turbine Systems
,”
5th International Conference on Computer Science and Education
(
ICCSE
), Hefei, China, Aug. 24–27, pp.
1299
1304
.
10.
Odgaard
,
P. F.
, and
Stoustrup
,
J.
,
2009
, “
Unknown Input Observer Based Scheme for Detecting Faults in a Wind Turbine Converter
,” 7th
IFAC
Symposium on Fault Detection, Supervision and Safety of Technical Processes
, Barcelona, Spain, pp.
161
166
.
11.
Chen
,
W.
,
Ding
,
S. X.
,
Haghani
,
A.
,
Naik
,
A.
,
Khan
,
A. Q.
, and
Yin
,
S.
,
2011
, “
Observer-Based FDI Schemes for Wind Turbine Benchmark
,”
18th IFAC World Congress
, Milan, Italy, Aug. 28–Sept. 2, pp.
7073
7078
.
12.
Kamal
,
E.
,
Aitouche
,
A.
,
Ghorbani
,
R.
, and
Bayart
,
M.
,
2012
, “
Robust Fuzzy Fault-Tolerant Control of Wind Energy Conversion Systems Subject to Sensor Faults
,”
IEEE Trans. Sustainable Energy
,
3
(
2
), pp.
231
241
.
13.
Rothenhagen
,
K.
, and
Fuchs
,
F.
,
2009
, “
Doubly Fed Induction Generator Model-Based Sensor Fault Detection and Control Loop Reconfiguration
,”
IEEE Trans. Ind. Electron.
,
56
(
10
), pp.
4229
4238
.
14.
Shashoa
,
N. A. A.
,
Kvascev
,
G.
,
Marjanovic
,
A.
, and
Djurovic
,
Z.
,
2013
, “
Sensor Fault Detection and Isolation in a Thermal Power Plant Steam Separator
,”
Control Eng. Pract.
,
21
(
7
), pp.
908
916
.
15.
Wei
,
X.
,
Verhaegen
,
M.
, and
van Engelen
,
T.
,
2010
, “
Sensor Fault Detection and Isolation for Wind Turbines Based on Subspace Identification and Kalman Filter Techniques
,”
Int. J. Adapt. Control Signal Process.
,
24
(8), pp.
687
707
.
16.
Luo
,
H.
,
Ding
,
S.
,
Haghani
,
A.
,
Hao
,
H.
,
Yin
,
S.
, and
Jeinsch
,
T.
,
2013
, “
Data-Driven Design of KPI-Related Fault-Tolerant Control System for Wind Turbines
,”
American Control Conference
(
ACC
), Washington, DC, June 17–19, pp.
4465
4470
.
17.
Guo
,
L.
, and
Wang
,
H.
,
2005
, “
Fault Detection and Diagnosis for General Stochastic Systems Using B-Spline Expansions and Nonlinear Filters
,”
IEEE Trans. Circuits Syst. I: Regular Pap.
,
52
(8), pp.
1644
1652
.
18.
Li
,
T.
, and
Guo
,
L.
,
2009
, “
Optimal Fault-Detection Filtering for Non-Gaussian Systems Via Output PDFs
,”
IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum.
,
39
(2), pp.
476
481
.
19.
Ren
,
M. F.
,
Zhang
,
J. H.
,
Fang
,
F.
,
Hou
,
G. L.
, and
Xu
,
J. L.
,
2013
, “
Improved Minimum Entropy Filtering for Continuous Nonlinear Non-Gaussian Systems Using a Generalized Density Evolution Equation
,”
Entropy
,
15
(
7
), pp.
2510
2523
.
20.
Kasem
,
A. H.
,
El-Saadany
,
E. F.
,
El-Tamaly
,
H. H.
, and
Wahab
,
M. A. A.
,
2008
, “
An Improved Fault Ride-Through Strategy for Doubly Fed Induction Generator-Based Wind Turbines
,”
IET Renewable Power Gener.
,
2
(
4
), pp.
201
214
.
21.
Karimi
,
S.
,
Gaillard
,
A.
,
Poure
,
P.
, and
Saadate
,
S.
,
2009
, “
Current Sensor Fault-Tolerant Control for WECS With DFIG
,”
IEEE Trans. Ind. Electron.
,
56
(
11
), pp.
4660
4670
.
22.
Lescher
,
F.
,
Zhao
,
J. Y.
, and
Borne
,
P.
,
2006
, “
Switching LPV Controllers for a Variable Speed Pitch Regulated Wind Turbine
,”
IMACS
Multiconference on Computational Engineering in Systems Applications
, Beijing, China, Oct. 4–6, Vol.
2
, pp.
1334
1340
.
23.
Boukhezzar
,
B.
, and
Siguerdidjane
,
H.
,
2010
, “
Comparison Between Linear and Nonlinear Control Strategies for Variable Speed Wind Turbines
,”
Control Eng. Pract.
,
18
(
12
), pp.
1357
1368
.
24.
Principe
,
J. C.
,
Xu
,
D.
, and
Fisher
,
J.
,
2000
, “
Information Theoretic Learning
,”
Unsupervised Adaptive Filtering
, Vol.
I
,
S.
Haykin
, ed.,
Wiley
,
New York
, pp.
265
319
.
25.
Li
,
J.
, and
Chen
,
J. B.
,
2010
,
Stochastic Dynamics of Structures
,
Wiley
, Singapore.
26.
Zhang
,
J. H.
,
Chu
,
C. C.
,
Munozb
,
J.
, and
Chen
,
J. H.
,
2009
, “
Minimum Entropy Based Run-to-Run Control for Semiconductor Processes With Uncertain Metrology Delay
,”
J. Process Control
,
19
(
10
), pp.
1688
1697
.
27.
Mao
,
X. R.
,
1997
,
Stochastic Differential Equations and Their Applications
,
Horwood Publishing
, Chichester, UK.
28.
Bououden
,
S.
,
Chadli
,
M.
,
Filalia
,
S.
, and
El Hajjajib
,
A.
,
2012
, “
Fuzzy Model Based Multivariable Predictive Control of a Variable Speed Wind Turbine: LMI Approach
,”
Renewable Energy
,
37
(
1
), pp.
434
439
.
You do not currently have access to this content.