Environmental Engineering Reference
In-Depth Information
performance of the resulting sensor FDI system is not affected by changes in the
reference signals and by disturbance variations. In addition, faults affecting the
rotor currents do not degrade the performance of the sensor FDI system. Besides, it
was observed that the control algorithm partially hides the fault affecting a par-
ticular rotor current sensor, and the effect of the fault is propagated to the other two
rotor current sensors.
10.7 Future Work
As wind turbines are growing larger and larger, the concern for online structural
health monitoring is increasing. This can notably be achieved through sensor
networks. Therefore, sensor monitoring for sensor networks appears to be an
important issue. This raises new problems such as the development of decentral-
ized methods allowing to handle a large number of sensors, and the determination
of proper ways to handle transmission delays and packet losses in sensor networks
in the framework of sensor monitoring. Initial work on this topic goes into two
directions: data-based methods [
24
], and model based methods [
25
]. In the later
case, the available results on decentralized state observers appears to be an
interesting starting point [
26
,
27
,
28
]. Open issues include the generalization of
classical multiobserver schemes, such as the generalized observer scheme and the
dedicated observer scheme, to sensor network while accounting for scalability and
decentralization. The extension of statistical change detection/isolation algorithms
to this context is also just starting to be tackled [
29
]. Another issue concerns
the integration, in the design of the FDI system, of the notion of risk (namely the
probability that a certain fault appears) and of the potential impact of a fault on
the process performance. The choice of the decision threshold should notably
account for risk and fault impact. This would allow one to go toward risk-based
maintenance.
References
1. Odgaard PF, Stoustrup J, Kinnaert M (2013) Fault tolerant control of wind turbines: a
benchmark model. IEEE Trans Control Syst Technol 21:1168-1182
2. Simani
S,
Castaldi
P
(2013)
Active
actuator
fault-tolerant
control
of a
wind
turbine
benchmark model. Int J Robust Nonlinear Control. doi:
10.1002/rnc.2993
3. Ciang CC, Lee J-R, Bang H-J (2008) Structural health monitoring for a wind turbine system:
a
review
of
damage
detection
methods.
Meas
Sci
Technol
19:122001.
doi:
10.1088/
4. Sattar TP, Rodriguez HL, Bridge B (2009) Climbing ring robot for inspection of offshore
wind turbines. Industr Robot: Int J 36:326-330
5. Crabtree CJ (2010) Survey of commercially available condition monitoring systems for wind
turbines.
http://www.supergen-wind.org.uk/docs/
. Survey of commercially available CMS for
WT.pdf. Cited 4 Dec 2013
