Environmental Engineering Reference
In-Depth Information
7.1 Introduction
Owing to inherent limitations in different kinds of the well-known fossil fuel and
nuclear energy sources, e.g. carbon footprint, rapidly increasing fuel prices or the
probability of catastrophic effects of nuclear station malfunction, the last two
decades have witnessed a rapid growth in the use of wind energy. Although, it is
considered a promising source of energy, depending on naturally generated wind
forces, there are several very significant challenges to efficient wind energy con-
version for electrical power transformation.
Wind turbine systems demand a high degree of reliability and availability (sus-
tainability) and at the same time are characterised by expensive and safety critical
maintenance work [
3
,
14
,
20
,
30
]. The recently developed offshore wind turbines
(OWTs) are foremost examples since OWT site accessibility and system availability
is not always ensured during or soon after malfunctions, primarily due to changing
weather conditions. Indeed, maintenance work for OWTs is more expensive than the
maintenance of onshore wind turbines by a factor of 5-10 times [
46
]. Hence, to be
competitive with other energy sources, the main challenges for the deployment of
wind turbine systems are to maximise the amount of good quality electrical power
extracted from wind energy over a significantly wide range of weather and operation
conditions and minimise both manufacturing and maintenance costs.
Since regular and corrective maintenance are among the factors that increase the
overall cost of wind projects, the most efficient way of reducing these costs would
be to continuously monitor the condition of these systems. This condition moni-
toring-based preventive maintenance allows for early detection of the degeneration
of the wind turbine health, facilitating a proactive response, minimising downtime,
and maximising productivity. However, the wind energy technical reports [
49
]
show that some of the currently available signal-based monitoring techniques are
unreliable and not suitable for wind turbine applications because of the stochastic
nature of the wind that affects the fault decision-making. Moreover, the simulta-
neous increase of wind turbine accidents with the increase of wind turbine size,
which are clearly shown in the failure records, such as the survey of failures in
Swedish wind power plant presented in [
35
] (see Fig.
7.1
), as well as the steady
increase of the number of OWTs projects have all stimulated research into fault
tolerant control (FTC) and fault detection and diagnosis (FDD) in this application
area since the ability to detect wind turbine faults and to control turbines in the
presence of faults are important aspects of decreasing the cost of wind energy and
increasing penetration into electrical grids [
3
,
12
,
20
,
30
,
38
,
39
,
42
].
Although the significance of wind turbine control on the overall system
behaviour is well investigated in the literature [
5
,
9
,
10
,
26
], nominal control
systems lack the ability to ensure system sustainability during components and/or
system faults. Clearly, improvements in FDD and FTC can play an important role
to ensure the availability of wind turbines during different normal or abnormal
operation conditions, minimise the number of unscheduled maintenance operation,
and
prevent
development
of
minor
fault
into
failure
especially
for
OWTs.
