Reliability and Maintainability - Wind Turbine

University of Manchester
Reliability & Maintainability
Group Project: Wind Turbine

Group G
Alistair Lambert
Artur Placha
Benjamin Holmes
Robert Smithers
Simon Hicks

Figure 1 (Farmery, 2013)

Table of Contents
Introduction 4
Wind Turbine Components 5
Reliability Analysis 7
FMMA Analysis 9
FMEA and FMECA Analysis 11
System Fault Tree Diagram Analysis and RBD 15
Failure Probability 19
Bowtie Analysis of Hazard Event 20
Results and Conclusions 21

List of Figures
Figure 1 (Farmery, 2013) 0
Figure 2 (Luminosity Engineering Technologies) 3
Figure 3 (Machine Design, 2001) 5
Figure 4 - Shows the Annual Failure Frequency and Downtime for Components of a Wind Turbine from Two Studies (Huhn) 6
Figure 5 - Shows the Probability Weibull and Reliability vs Time Respectively 7
Figure 6 - Shows the Probability Density Function and Failure Rate vs Time Respectively 7
Figure 7 - (Energies Doc, An FMEA-Based Risk Assessment Approach for Wind Turbine) 10
Figure 8 - Top 3 Levels FTA (Simplified) 2
Figure 9 - Reliability Block Diagrams 3
Figure 10 - Graph Showing Probability of Failure vs. Operational Time 3
Figure 11 - The Pie Chart for Failure Probability Based on the Table In The Appendix 4
Figure 12 - Shows the Bowtie Analysis for Fire Event 5
Figure 13 - Schematic Overview of Different Maintenance Types 6

Introduction
With diminishing fossil fuels and the dramatic intensification of greenhouse gas pollution, there have been numerous developments in alternative renewable energy sources (IPCC, 2013). This has led to an increase in the number of wind farms around the world over the past three decades, with them becoming a critical part of many countries electrical network (E.ON, 2004). Countries are now becoming reliant on the contribution of electrical energy from wind turbines, with Denmark receiving 33% of their energy in 2013 from wind power (Vittrup, 2014). It is therefore essential to recognise the reliability of a wind turbine and its sub-systems.
This report aims to explore the reliability of different components of a wind turbine using multiple different reliability analysis techniques. Firstly this report will include a brief overview of the major components. From this it will be possible to determine particular areas that will need to be focused on, either due to the likelihood of a failure event to occur or the estimated cost and downtime of the system if that particular component were to fail.
These components are then to be analyses using failure mode and maintenance analysis (FMMA). This essentially recognises the main components that maintenance and repair action will likely be required. These areas must then individually be considered and appraised. Components from the different wind turbine subsystems will be evaluated in a joint FMEA and FMECA table. This will focus on the components which are thought to be most likely to fail, and not ones that do so randomly, to give an idea of what can actually be achieved to detect and prevent these. An important extension of the failure mode analysis will be to criticality section the table to give quantitative results on how much consequence these failure modes have on the whole system.
A system fault tree will be completed which will give a much deeper analysis into the overall reliability of the wind turbine system. One subsystem will be analysed up to ten levels, to demonstrate the detail that this method has.
Following this, a bow tie analysis for a fire will be produced; this is to look at the different causes and the different outcomes of a catastrophic failure of the system. A pie chart of the probability of failure for each component will also be included in order to graphically represent and easily identify the most and least likely component that will fail.

Wind Turbine Components
Anemometer and Wind Vane:
The anemometer measures the wind speed and conveys this data to the controller. The wind vane measures wind direction and communicates with the yaw drive to orient the turbine properly with respect to the wind. If either of these were damaged it could have a detrimental effect on the wind turbine as false wind speed and direction could be fed to the controller. This could result in the rotor being damaged due to use in off-design wind conditions, for example too high a wind could lead huge rotor damage and total failure of the wind turbine.
Blades:
The blades are essentially what converts the energy from the wind into rotational energy that runs the generator. The blades essentially work as an aerofoil as the wind passes over the blades, with the wind producing lift and thus causing the rotor to rotate. If one of the blades were to fail, from the structural integrity of the blade to the blade bearing wearing out, it could cause to the wind turbine to run inefficiently or not work at all.
Brake:
The brake is a requirement of the wind turbine in order to reduce the rotational velocity of the rotor in high wind speeds or as a failsafe for the system, in case the system has to be shut down due to an emergency. Both of these situations require a break to reduce the risk of damage to the wind turbine.
Controller:
The role of the controller is to bring the wind turbine into the optimal operating state and then adjust it accordingly as conditions change. It does this by reading data from several sensors from different areas. If this was to fail, the wind turbine would not be set up in the most efficient state, but it is unlikely to cause a catastrophic failure.
Nacelle:
The nacelle is the housing that sits at the top of the tower, housing the generating components of the wind turbine. It is generally made out of fiberglass to not be load bearing. On larger off-shore wind turbine however, the nacelle has to be large enough to have a landing pad for a helicopter, to allow for easy access for maintenance and repair crews.
Pitch System:
The pitch system controls the angle of attack of the blades of the rotor. This is to ensure that the rotor is running at the most efficient and that the wind turbine changes to particular conditions. The pitch system is governed by the turbine’s electronic controller this is done by measuring the readings from the anemometer and the turbine’s power output. A failure in the pitch system could lead to the system being damaged, with the rotor spinning too fast in high wind conditions.
Rotor:
The blades and the hub together are known collectively as the hub. The rotor diameter is directly linked with the amount of power that the turbine can produce. The rotor diameter is directly related with the amount of power that the turbine can produce. As the rotor diameter increases, the power production increases.
Tower:
The tower is the main structure that supports the turbine and therefore it is imperative to ensure the structural integrity of the tower can withstand the expected loads. This is due to the fact that wind speed increases with altitude, higher towers enable wind turbines to deliver a larger amount of electricity. The downside is that the wind turbine becomes more expensive due to the increased amount of materials needed to construct the tower and support it.
Yaw Drive System:
The yaw drive orients the nacelle so that it points into the wind and ensures that the wind turbine works most efficiently in with the wind direction. The nacelle is moved using the yaw motor and a failure in this system would mean the wind turbine could only face one direction resulting in the system being useless most of the time, but not in much danger of leading to a catastrophic failure.
Drivetrain
The drivetrain is one of the most critical components of a wind turbine, without it the turbine will produce no electricity. It is, however, the most unreliable sub-system within the wind turbine, with the industry experiencing high gearbox failure rates from the creation of wind farms (W. Musial, 2007). This paper therefore pays particular attention to these components as, in practice; this is the area that fails most.

Gear Box:
The gear box increases the revolutions per minute of the rotor to an adequate rotational velocity to power the generator. The rotor hub is connected to the low-speed shaft which runs into the gearbox. The high-speed shaft runs out of the gear box and should be running around 30 times the original speed. This new speed is the rotational velocity that is necessary to run most generators to produce sufficient electricity.
Generator:
The generator uses the properties of electromagnetic induction to produce electrical voltage from the rotational motion from the high speed shaft. The use of an induction generator is useful due to the ability of to produce useful power at different rotational speeds.

Reliability Analysis
Reliability analysis is hugely important to a variety of life predicting applications as well as for predicting when maintenance needs to be carried on products. This is done by fitting statistical distributions to given life data, which is used as a representative sample, to predict important life characteristics of components and parts. One such reliability engineering resource is the Weibull software which from parameterised distribution data can give a reliable estimate of the probability of failure at a specific time. But also the mean life and expected failure rates based on previous gathered data and within prescribed confidence bounds.
Therefore the objective of this section is to identify failure modes and discuss the reliability analysis for particular the sub-sections of the wind turbine discussed in the previous section. The following FMMA section will discuss how particular parts can fail in more detail but it is important to predict the time to fail so that the design and maintenance of a wind turbine is efficiently run for of particular components.
Accurately predicting the failure rates is important however the downtime of the turbine as faults are fixed must also be considered. Hence, increasing the life of a component that shuts down the wind turbine for a long time, even if less often, will have a more positive effect on turbines overall operation. Components that take longer to fix are also generally more costly due to higher labour costs, the costs of the parts and the resulting loss of energy production and corresponding profit. Following is a breakdown of affected components, showing annual failure frequency and the down times for the corresponding fault. The first table is taken from a lecture by Roger Hill at the Sandia National Laboratories and the second from a Reliawind Report on Wind turbine Reliability Profiles. The later shows supporting data collected from 2 large European Surveys, LWK and WMEP. It was decided to include both sets of graphs to highlight how statistical data can often be interpreted differently, with variances in all three sets of results.

Figure 4 - Shows the Annual Failure Frequency and Downtime for Components of a Wind Turbine from Two Studies (Huhn)
Both graphs agree that the Gear Box, Rotor blades and Drive Train are that cause the greatest downtime despite having smaller failure frequencies. Therefore these are the ones to be considered most critical. Following this, cross referencing our collected data for the Drive Train Module it also includes the gear box and the Power Module the generator and the Rotor Module the rotor blades. It was then necessary to collate life data on these components and subsystems so that life time distributions could be calculated. However, after lengthy research it was only possible to find trusted failure data for the main shafts, main bearings, gear box and generator of a 600 kW Wind Turbine. Therefore, this only represents the gear box and the three major failure areas of the drive train including generator. This information was taken from Jesse Agwandas Andrawus’s thesis titled “Maintenance Optimisation for Wind Turbines” and the compiled data tables are given in the appendix (Andrawus, 2008). This data was taken from a survey of different wind turbine designs at different wind farms and the appropriate data has been taken from the information supplied. For the gear box and generator there was particular failings within the bearings, gears, key way and windings and bearings respectively. This would sometimes cause a catastrophic failure and hence the data includes suspended terms where the turbine would continue to be operational but would require maintenance to stop further damage. Weibull software can be used for data sets that do not include failures yet should be considered. This information is essential to identify re-occurring failure modes and to effectively trace the failure history of each component or subsystem of the wind turbine. The charts for Probability – Weibull, Reliability vs Time, Probability Density Function and Failure Rate vs Time Plot are fully shown below and over-layered for blue = bearings, black = gearboxes, green = generator and pink = main shaft.

Figure 5 - Shows the Probability Weibull and Reliability vs Time Respectively

Figure 6 - Shows the Probability Density Function and Failure Rate vs Time Respectively
From these results it is shown that the reliability for these four components can be used to predict their failure life. Setting a year as a target mission life before failure occurs can calculate (from the Quick Calculation Pad) the reliability that each of the four components will reach this the probability of failure within this time and failure rate for one year as well as the Mean Life of the Component with a required reliability of 0.85.

Component
Reliability of Component for 1 Year
Probability of Failure for 1 Year
Failure Rate (/year)
Mean Life (year)
Bearings
0.462
0.537
0.913
1.176
Main Shaft
0.831
0.169
0.438
1.806
Gearboxes
0.952
0.048
0.057
12.946
Generator
0.964
0.036
0.035
33.482
Full System
0.802
0.198
0.361
12.35

This clearly shows that the Generator is the most reliable component of the four with the gearbox following in second. The bearings are the most likely part to fail and have the highest failure rate and close 1 meaning that they are likely to fail in 1 year. This means that constant maintenance is needed on the bearings and the shaft, which is part of the Drive Train Module and also one of the most time consuming to fix. The generator which can also cause a lot of down time is luckily the most reliable component of the four with a very small failure rating per year. The summation of these components has also been shown to give a better overview of the whole wind turbine.
For the overall system, statistically the mean life is over 12 years even though other parts are likely to fail and down the system before this.
FMMA Analysis
The Failure mode analysis of the wind turbine is an important part of the design and needs to be presented hand in hand with the reliability analysis of the systems components. This is the consideration of how a machine or system may fail, whether welcomed or not by engineers and can be done to a range of complexities. The method employed in this section will identify the principal failure modes for 6 subsystems and components and consider the required maintenance and repair actions required. These will include actions and procedures that are required to repair the equipment in a reasonable and straightforward manner as well as the feasibility of any routine maintenance procedures or condition monitoring methods that may be required to prevent these failures before they happen. Recommendations are also to be given for where particular attention should be made to achieve high maintainability and reliability. It is also important to note that there are certain critical failure modes that affect the operation of the turbine in different ways and may not necessarily disrupt its electrical output. It is important to understand the severity of faults and how it reflects on down time and commonly broken down into 4 categories:
Category 1: Manual restart - Requires physical presence by crew at the turbine to reset tripped alarms and software rather than physical repair. Can occur frequently but is a short fix; investigation into the cause is needed.
Category 2: Minor repair - Typically sensor or instrumentation failure and the replacement of small parts is needed alongside further troubleshooting making it quite time consuming due to required investigation and repair. The purchase of parts is often required.
Category 3: Major repair - Major mechanical repair or replacement of a significant component is needed, usually the gearbox, shaft bearings, blades, control systems or generator. Occurs less often but is very time consuming and can be costly depending on the component. Taking days to complete and test before operation can begin again.
Category 4: Major replacement - Most often early life failure due to build defects or in later life the replacement of significant components, the geography and station of the wind farm can mean highly skilled crew are needed resulting in a lengthy time scale for repairs.
Gear Box
Principal Failure Mode
Cat.
Probability
Required Maintenance/Action
Avoidance/Recommendations
General Fatigue – Hertzian and Bending
2/3
Common
Review of all components and replacement of damaged parts or whole gear box and increase lubrication
Review of the design of the gear box and the build materials to prevent further cases in new builds or to increase the life of used parts – contact stress and surface hardness
Over Load – Fracture and Deformation
2/3
Low /Common
Review of all components and replacement of damaged parts or whole gear box
Often on the bearings and shaft, future designs alterations must be made to prevent recurrent damage
Over Heat
1
Low
Manual restart
Thermal analysis to prevent future recurrence and review of cooling mechanism possible alteration to alarm set up
Vibrational Damage
2/3
Very Low
Review of all components and replacement of damaged parts or whole gear box
Caused by poor build and alignment errors, production techniques must be revised
Contamination
2
Low
Cleaning and re-lubrication of gears
Correct use of lubricants and review of filtration devices is needed
Generator
Voltage Irregularities
1
Low
Manual restart
Review of cause and electrical components
Contamination
2
Low
Cleaning
Review of filtration device and particular geographical conditions
Electrical Storms
1
Very Low
Manual restart
Shut down turbines during storms
Bearing Failure
3
Common
Review of all components and replacement of damaged parts or whole generator
Review of the design of the generators parts and the build materials to prevent further cases in new builds or to increase the life of used parts
Over-Speed
2
Low
Review of all components and replacement of damaged parts or whole generator
Review of the design of the generators parts and the build materials to prevent further cases in new builds or to increase the life of used parts
Rotor Lead Failure
2
High
Review of all components and replacement of damaged parts or whole generator
Thermal analysis to prevent future recurrence and review of cooling mechanism
Over Heat
1
Common
Manual restart
Thermal analysis to prevent future recurrence and review of cooling mechanism possible alteration to alarm set up
Yaw System
Fatigue in Shaft
2/3
Low
Review of all components and replacement of damaged parts or whole system
Review of the design of the system and the build materials to prevent further cases in new builds or to increase the life of used parts – contact stress and surface hardness
Fracture in Gear Teeth
2/3
Low/Common
Review of all components and replacement of damaged parts or whole system
Review of the design of the system and the build materials to prevent further cases in new builds or to increase the life of used parts – contact stress and surface hardness
Bearing Failure
2/3
Common
Review of all components and replacement of damaged parts or whole system
Review of the design of the system and the build materials to prevent further cases in new builds or to increase the life of used parts – contact stress and surface hardness
Motor Failure
3/4
Low
Review of all components and replacement of damaged parts or whole system
Review of the strain on the motor and the use needs to be reviewed and potentially a better motor is needed
Pitch System
Hydraulic System
2/3
Common
Commonly failures in the pump and oil leakages which need to be repaired
Estimation on failure rates for timely maintenance will increase life
Broken Wind Vane
2
Low
Review of all components and replacement of damaged parts or whole system
Review of the design of the system and the build materials to prevent further cases in new builds or to increase the life of used parts
Faulty Anemometer
1/2
Low
Manual restart, failing that site inspection and repair
Possibly due to calibration or material damage. Review of geographic conditions needed
Electrical
1/2
Low
Manual restart, failing that site inspection and repair
Increase protection to electrical systems due to environmental effects
Brake
Worn Pads
2/3
High
Review of component and replacement of damaged parts
Easy to predict and replace
Contamination
2
Common
Cleaning of grease /oil/dirt
Review of filtration device and particular geographical and environmental conditions
Over Heat
1
Low
Manual restart
Thermal analysis to prevent future recurrence and review of cooling mechanism possible alteration to alarm set up
Nacelle
Fire
4
Very Low
Sit Rep and Major Replacement Need
High risk management and fail safes should be in place
Structural Integrity
3/4
Very Low
Review of components within the nacelle and replacement of damaged parts
Review of geographical location and environment for re-design of materials

FMEA and FMECA Analysis
The purpose for doing FMEA and the extension FMECA analysis was to investigate the failure modes for 14 different components from the 9 different subsystems chosen for wind turbines. The causes, effects, severity and criticality of each failure mode was then analysed and tabulated which can be seen in the joint table shown in figure 8. The criticality of the failure modes extends the FMEA analysis to FMECA, and is used to give a measure of proportional effects on the system for each of the failure modes given based on frequency and consequence. The data used in the table was acquired from WindStats Germany, WindStats Denmark and LWK (Lange, Wilkinson, Delft, 2011). This data featured the average amount of failures per year for a turbine. One value from each of the parties mentioned and then averaged together to give a value of turbine yearly failure rate, and then this was extrapolated and computed with ‘Percentage contribution to Total Failure Rate’ from the Reliawind data to give the value of ‘Total Failure Rate’ (ʎp) for each component as seen in the table.
Assumptions and Remarks
Some assumptions were made in the construction of the table, and it should be noted that these apply to severity, failure mode effect proportion (α), and the probability of the failure effect occurring (β). The severity scale used for grading the different failure mode effects can be seen in figure 8 below, and these were applied using the rationale of how much of the system’s usability they effect, how long the system would have to be down for and the cost of such events occurring.

Figure 7 - (Energies Doc, An FMEA-Based Risk Assessment Approach for Wind Turbine) Subsystem
Item Description
Function
Failure Mode
Failure Cause
Failure Effect
Failure Mode Detection
Compensating Provisions
Sev-erity
Class
Loss Frequency

Local Effect
System Effect

ʎp (F/MH) α (%) β (% Prob)
ʎ0 (F/MH)
Auxiliary Equipment

Electrical Protection & Safety Devices

Protection of circuitry and systems from excess current and shorting damage
A. Electrical Failure
B. Over speeding
C. Loss of Yaw control
D. Exceed Power Rating
Break in circuitry of the auxiliary system. Fracture and failure of mechanical safety devices.
A. No Control
B. Temperature increase and power overloading
C. No directional control
D. Increase in Temperature
A. Divergence of performance parameters.
B. Excess system stress
C. Excess strain on speed control, at mercy of the wind
D. Potential System Burnout
Monitoring and Testing of Electrical failsafe and mechanical safety devices
Integrated UPS systems that can provide backup power for 12 hours in case of emergency. This gives time for the problem to be defined and for a response to be prepared.
8

10

7

8
2.53
25

30

40

10
100

100

100

50
0.63

0.759

1.012

0.127
Condition Monitoring System
Data Logger
Generation and storage of system condition logs
A. Failure to Calibrate
B. Battery Failure
C. Connector Failure
A. Human Installation Error
B. Length of time of continuous use (over discharge)
C. Water exposure or Oxidation
A. Errors in data acquisition
B. Loss of power and usability of component
C. Intermittent data acquisition
A. Incorrect data sent to operator
B. No received data packages, no system awareness
C. Only Intermittent system monitoring possible
A.B.C Regular testing and checking of respective electronics to ensure all work as they should
A.B Some data logging capability in other components, failure may not lead to complete termination of data logging capability
C. anti-oxidation covering
4

3

2
7.80
1

15

20
20

100

60
0.016

1.17

0.94
Control and Communication System
Sensors
Measurement of system and environment parameters
A. Contamination
B. Static Discharge
A. Exposure or leaks onto sensitive component part
B. Residual charge on humans transferring during maintenance
A. Incorrect values given from component
B. Can render sensor unusable
A. Unreliable system property knowledge
B. Termination of data acquisition
A. Servicing of sensitive measuring equipment
B. Earth wire bracelets to prevent residual static charge
1

1
0.02
70

30
70

80
9.8x10-3

4.8x10-3

Communication System
Continuous transmission of status, performance and diagnostic data to control centre
A. Ethernet Cable Failure

B. Modem Failure
A. Weathering due to exposure to elements

B. Internal degradation
A.B. Loss of connection
A.B. Loss of situational awareness
A.B Communication Testing
A.B. Quick response unit for what often can be a quick fix
3

4
7.41
20

80
100

100
1.48

5.71
Drive Train Module
Gearbox Assembly
Conversion of wind turbine rotational speed to a level suitable for power generation
A. Gear Failure - Fatigue Cracking
B. Bearing Failure - Wear and Pitting
C. Bearing Failure - Abrasion (Oil Filter)
D. Lubrication System Failure - Filter Failure
E. Lubrication System Failure Pump Failure
A. Excessive tooth loads
B. Accumulation of foreign particles
C. Absence of lubrication
D. Use of lifespan
E. Low quality/contaminated hydraulic fluid
A. Decreased structural integrity and effectiveness of gear
B. Increased friction and part grinding leading to increased wear.

A.B.C.D.E All lead to potential failure of Gearbox assembly, however some at different rates. Causes a loss of speed and torque control
A.B.C.D.E Detect defects in gear assembly material through inspection bore scopes and also accelerometers for vibrations measurement. Test for contamination of lubricating fluid
A.B.C.D.E Quality Assurance of installation and component gearing parts. Alignment and manufacturing tolerance critical
8

7

7

7

7
9.85
20

30

10

15

15
100

100

100

100

100
1.97

2.96

0.99

1.48

1.48
Nacelle Module
Yaw System
Alignment of turbine orientation to match oncoming wind direction
A. Motor Failure
B. Bearing Failure
C. Controller Failure
A. Accumulation of moisture and dirt, misalignment
B. Stick Slip Effect
C. Power loss
A. Motor misalignment causes unwanted vibration
B. High wear and sound emissions
C. Loss of yaw movement capability
A.B.C. Without Yaw system, over speeding and lack of control leads to critical system situation. Shut down necessary
A.B.C Inspection of yaw system, control testing and accelerometer placement for vibration testing
A.B.C Quality assurance of installation and parts. Pitch system use can negate some consequences of failure
8

8

9
21.66
30

30

15
100

100

80
6.50

6.50

2.6
Power Module
Frequency Converter
Conversion of generator output frequency to match grid frequency
A. PCB Failure
B. Solder Degradation
C. Capacitor Failure
A. Warping due to thermal profile variance
B. Errors in soldering and oxidation
C. Over charging and end of lifespan
A. Break in circuit, termination of usability
B. Contacts become loose, render components ineffective
C. Intermittent capability of subsystem
A.B. System cannot handle the high variances in electrical loading as a function of wind speed
C. Some capability still remains for control over electrical loading.
A.B.C Inspect and test component. Thermometer for temperature measurement
A.B.C Quality assurance of component manufacture. Yaw and Pitch system use can negate consequences of failure
7

6

7
24.88
30

10

10
100

50

80
7.46

1.24

1.99

Generator Assembly
Mechanical-to-electrical energy conversion
A. Bearing Failure
B. Windings Failure
C. Contamination
A. Inadequate lubrication
B. Winding insulation deterioration
C. Incursion of water, lubricant or dust
A. Surface scraping
B. Local short-circuit
C. Local short-circuit
A. Reduced mechanical efficiency, overheating
B.C. Reduced electrical efficiency
A.B.C. Observed deterioration in power output
A. Internal temperature monitoring

7

5

5
13.75
40

30

10
100

100

20
5.50

4.13

0.28

Switch Gear
Isolation, control of electrical output
A. Water Incursion Failure
B. Connector Failure
C. Ground Protection Fault
A. Casing breach, exposure to condensation
B. Improper maintenance
C. Improper installation
A. Severe short-circuit
B. Loss of electrical connection
C. Arcing leading to subsystem damage
A.B.C. Loss of power transmission
A.B.C. Sudden loss of transmission, operating system alert
C. Current injection tests

8

8

8
8.83
50

10

10
100

20

50
4.42

0.18

0.44
Rotor Module
Pitch System
Optimization of blade pitch in response to changing wind speed
A. Pitch Motor Failure
B. Pitch Controller Failure
A. Moisture incursion
B. Solder degradation
A.B. Loss of blade pitch authority
A.B. Reduced efficiency
A.B. System damage risk in high wind conditions
A.B. Lack of reaction to control inputs

7

7
40.88
40

20
100

80
16.35

6.54

Blades
Conversion of wind energy into rotational motion
A. Structural Load Failure
B. Impact Damage
C. Over speed
A. Manufacturing/assembly error
B. Airborne object impact
C. Brake failure in high wing conditions
A. Blade fracture and separation
B. Blade skin damage
C. Rotor disintegration
A. Turbine in operational
B. Loss of aerodynamic efficiency, increased risk of structural failure
A.B.C. Visual inspection

A.B. Impact sensor
A.C. Adequate ground exclusion zone
10

4

10
2.78
10

10

30
100

10

60
0.28

0.028

0.50
Structural Module
Tower
Turbine support, maintenance access
A. Buckling Failure
B. Impact Damage
A. Man. error
B. Airborne object impact
A. Tower collapse
B. Anticorrosive layer damage
A. Total loss of turbine
B. Exposure to corrosion
A.B. Visual inspection
B. Impact sensor
A. Adequate ground exclusion zone
10

3
5.11
2

1
100

20
0.10

0.010

Foundations
Tower support, anchoring (offshore)
A. Vessel Collision
B. Concrete degradation
A. Navigation error
B. Exposure to elements
A. De-anchoring
B. Cracking
A. Loss of power generation, likely total loss of turbine
B. Premature decommissioning
A. Perimeter monitoring alert, sudden loss of turbine output, vessel emergency communications

10

8
1.34
5

30
90

100
0.060

0.040
Wind Farm
Wind Farm System
Interfacing individual turbines with the grid
A. Transformer Fire
A. Overheating
A. Loss of transformer station
A. Loss of wind farm output
A. Flame/heat sensors, visual inspection
A. Automatic fire extinguisher system
10
1.36
5
100
0.068
System Fault Tree Diagram Analysis and RBD
Fault Tree Analysis is a ‘Top-down’ method used to assess the reliability of a system. It is used to understand why systems fail and identify the best ways to mitigate risk. By considering the main fault of a system, reasons for why that fault occurs are considered and are added to the next level of the tree. This process continues until sub-systems components are isolated and no other faults can be found. This deductive method allows for smallest of faults to be taken into consideration.
The fault tree is composed of symbols which describe what type of fault occurs, using ‘events’ and logical gates’. The root event, branches off using an ‘AND’ or ‘OR’ gate which describes what events can occur next. The ‘OR’ gate suggests that only one of the following intermediate events occur, whereas an ‘AND’ gate suggest that all events after the gate will occur. The primary events used in the FTA consist of, a ‘Basic Event’ which is a failure or error in a subsystem component, an ‘External Event’ which will occur outside the boundaries of the system, an ‘Intermediate Event’ which is occurs between two gates and an ‘Undeveloped Event’ which is a failure or error with insufficient information for analysis.
An advantage of Fault Tree analysis is that it maps out a wide range of faults of a system, which allows engineers to deduce what is wrong with a system very quickly. This is especially helpful when a system is complex and contains many components. However Fault Tree analysis can be misleading if multiple engineers contribute to the construction of the fault tree, as faults can overlap and be considered multiple times. Another disadvantage FTA has is that it doesn’t consider a partial failure. The system can still be operational, however in the Fault Tree sees the system either being fully operational or in failure mode.
In the case of our wind turbine, the tree branches off in many directions, however only one leg has been explored fully, with 12 levels of analysis. As complexity of a system increases, the tree also becomes more complex, resulting in an increased chance of failure is failures are not properly identified and mitigated against. In engineering applications where human life is at risk, it is more important that safeguards are in place in order to reduce the chance of failure. The fault tree consists of many undeveloped events, where reasons for their failure haven’t been fully explored. For complete analysis of the Wind Turbine system, the aim to create a tree where no event is undeveloped and the ends of the branches end with basic or external events.
As the Fault Tree is composed of many ‘Or’ gates it means that the entire system can fail from a single component failing. Therefore it is important to determine the failure rates of individual components, to assess the reliability of the whole system. Risk can be mitigated throughout the fault tree in many ways and as a result, the reliability of the whole system can be improved. For example the chance the gears failing are dependent on, material quality, alignment of components and temperature. Therefore during the design phase of construction of the wind turbine, a higher quality of material can be chosen. During manufacturing, extra care can be taken during the assembly of the gearbox and can be inspected by senior/more qualified employees. When the turbine is in operation, making sure proper maintenance is carried out is essential for the up keep and reliability of the wind turbine. As temperature affects the reliability of the gears, it is essential that they are properly lubricated and the oil used is cooled and kept to a high quality. The fault tree allows many areas of system to be highlighted and vast array of components can be displayed graphically.

To calculate the reliability of the wind turbine as a whole, the reliability block diagram method can be used. This method uses the fault tree to isolate components and calculate reliabilities working up to the main event failure. This method demonstrates how certain components and subsystems contribute to the success or failure of the system. This method was conducted on the top three levels of the fault tree as failure rates for sub-systems could be collated and reliability data can be obtained.

Figure 8 - Top 3 Levels FTA (Simplified)
Figure 2 is a simplified version of the fault tree. The data given has been collated and percentage contribution to total failure has been grouped into appropriate subsystems.

Table 1 displays the percentage contribution of the total failure rate of the wind turbine data set. As the data collected uses 35000 downtime events from 350 turbines over 450 months, this results in a turbine failing approximately 2.6 times per year. Therefore in order to calculate the failure rate per year as an actual value, the percentage is divided by 2.6. By using the following equation, the failure rate can be transformed into a reliability value. Where R (t) is the reliability, is the failure rate and t is the duration, in our case it is one year Table 1 Contribution to total failure rate
Failure Rate per year
Reliability
Drivetrain
6.42%
0.024071991
0.976215429
Yaw System
11.28%
0.042294713
0.958587231
Rotor
24.64%
0.092388451
0.91175091
Auxiliary
4.88%
0.018297713
0.981868674
Tower
2.66%
0.009973753
0.99007582
Foundation
0.70%
0.002624672
0.99737877
Control
16.39%
0.061454818
0.940395434
Generator (Power)
32.66%
0.122459693
0.884741566

Once the reliability for each subsystem is established for the first three levels, the reliability block diagram method can be used to assess the reliability of the wind turbine as a whole. This method shows how a component or subsystem contributes to the failure or success of a system. As only ‘OR’ gates were used in the fault tree analysis, the ‘blocks’ are in series and can be multiplied to obtain the reliability of the above level.

Figure 9 - Reliability Block Diagrams
Therefore after conducting the reliabilty block diagram, the reliabilty of the wind turbine is 0.534. Thus the probabilty of the failure of the wind trubine can be calculated using;(0.466)

The failure rate is then obtained by transforming the probability of failure equation.

This value can now be used to calculate the mean time to failure (MTTF). years
After conducting RBD analysis on the wind turbine it has been deduced that the system will fail on average in approximately 1.31 years. This graph supports this fact as at 0.5, the average probability, the operational time is close to the MTTF.
This graph also depicts that as operational time increases; the chance of failure also increases logarithmically, with near 100% chance of failure after 5 years.
Ideally this analysis should be improved on, creating a full FTA and conducting RBD analysis on the full system. This would allow for high risk components to be highlighted and measures can be taken in order to mitigate chance of failure, such as redundancy measures and improved maintenance.
Failure Probability

Figure 11 - The Pie Chart for Failure Probability Based on the Table In The Appendix
This diagram presents failure probabilities for wind turbine modules in the inner ring, taken from the Reliawind Deliverable D.1.3. This is further broken down into individual component failure probabilities for each system on the outer ring. All components responsible for less than 1 percentage point of failures were grouped together in the “other” subcategory for their respective module. Furthermore the least failure-prone modules for below a 5% point threshold were also banded together in the “other” category.
This form of representation allows for easy identification of the most- and least-vulnerable modules and components. Additionally, the outer ring provides a clear indication of the most vulnerable components in each module. The diagram indicates the division into modules where problems appear primarily in just one component, such as the Nacelle Module suffering from Yaw System faults, and modules with a variety of failures making up the overall score, as in the case of Power Module.

Bowtie Analysis of Hazard Event

Figure 12 - Shows the Bowtie Analysis for Fire Event
The diagram above represents a Bow Tie analysis for wind turbine fire event. To the left, possible sources of ignition have been indicated and prevention barriers have been identified for each of the possible causes and marked as pertaining to design (yellow) and operational procedures (green) of the turbine. Of particular importance is leak prevention and early detection as the nacelle houses significant amounts of highly flammable fluids (gearbox lubricant, hydraulic fluids, and transformer oil) but is otherwise practically non-flammable. Should leaks occurrence be minimized, presence of any of the ignition sources indicated in the diagram may not escalate to an open flame not having found any fuel to ignite.
The right hand side of the diagram indicates the consequences and mitigation barriers once a flame has appeared in the turbine. The primary mitigation barrier is a Fire Extinguishing System, if present and operational, it may prevent the fire from escalating. If it fails, design features of the turbine may limit the damage it does before going out naturally. The damage ranges from subsystem-level to complete loss of turbine.
Damage sustained by the turbine is not affected by any post-factum actions taken on the ground and there is currently no effective way of extinguishing a turbine fire once it occurs because the fire-fighting equipment is incapable of generating sufficient pressure to deliver the extinguisher fluid to nacelle altitude. Actions taken on the ground in these cases normally focus on perimeter control in order to prevent secondary fires caused by flaming debris and this risk can also be mitigated by observing adequate public exclusion areas around wind turbines.
Independently from the damage done to the systems, should a fire occur while maintenance personnel is working in the nacelle, an appropriate emergency descent system is necessary to prevent injury and death.
Results and Conclusions
In summation several methods of reliability analysis have been carried out on a wind turbine with a variety of results and overall positives and negatives.
Reliability Analysis
This argues that reliability analysis needs to be considered on all components to give an accurate prediction of the wind turbine. Further work however needs to be considered on the way in which parts fail, as mentioned a failure of a part in the gear box doesn’t necessarily mean complete shutdown of the turbine and may only affect performance and efficiency of power output and therefore it must be considered with the Failure Mode and Maintenance Analysis.
FMMA
Having discussed and studied the way in which 6 components can fail and how these can be avoided it was significant with reliability analysis that the down time is the crucial factor in the turbines operations success. The number of inspection visits and corrective maintenance actions must be lowered to reduce running costs and downtime is hugely important to the goal of delivering a power quality and quantity whilst persistently ensuring profitability over the lifetime of the offshore wind farm. Maintenance falls into two broad categories; preventive and corrective and a detailed breakdown is illustrated.

Figure 13 - Schematic Overview of Different Maintenance Types
Preventive maintenance consists of calendar-based maintenance in line with the predicted failure rates from Weibull and other analysis and condition-based maintenance which is the measured health of the system by software so that the system can be inspected off-site and appropriate maintenance ordered. Corrective maintenance consists of planned repair, based on the observed degradation of components from on-site visits to predict the expected time to fail. For example undertaking full inspections of the system, reviewing parts and logging when maintenance is expected to be needed on the turbine. Whereas unplanned maintenance is that which is necessary after an unexpected failure of a system or component and is commonly noticed after full shut-down of the system. In conclusion bigger and newer is not always more reliable as the maintenance is the critical factor affecting machinery life. For a turbine confident monitoring and plan for repair has proven much better than a running a system to failure, making life prediction of components incredibly important.
FMEA and FMECA
The decision was made at the start of the analysis to use components from every subsystem of the wind turbine. A lot of the components chosen had a relatively high contribution to wind turbine failures, however not all of them. The analysis of sensors is an example of a component that has an extremely low failure impact on the system. The component loss frequency is of the order of 10-3, and so it could be said that this and components like it can fail on an almost random basis which means that it can be very difficult to effectively predict and compensate such failures. This result also implies that failure here does not have a huge effect on the overall system.

The component that has the biggest contribution to turbine failure is the Pitch system from the Rotor module subsystem. This component is important for speed control and adapting to the wind conditions. This therefore is a high risk, non-reliable part of the system that should be the focus for improvement to decrease turbine failures in the most cost effective fashion.
There are ways that this FMECA could be improved. Firstly, there were a lot of assumptions that had to be made regarding the failure effects and probabilities of the effects on the system. It could be said that some higher fidelity analysis could be undertaken in these particular areas, so that the precision of the criticality of the FMECA could be improved. Secondly, there are a lot of variables that could not be accounted for; such as the long term behaviour of the wind, and the amount of operating time wind turbines execute seasonally. If these were accounted for the failure rate would be more accurate, and therefore the usefulness of the analysis as a whole would be increased. Finally there was a limited amount of components and failure modes that could be considered for this exercise and so here lies a limitation in that only a fraction of the whole system was analysed. In reality, a vast table would be necessary to be able to achieve a complete FMECA of the system, that could then be used to in improving wind turbine systems by decreasing failure occurrence and increasing reliability.
Fault Tree Analysis and RBD
The FTA analysis conducted allows the reliability of the Wind Turbine to be assessed taking into account failures at component level. This allows the full spectrum of the assembly to be considered and components which can be seen as high risk (contribute to a larger proportion of failure) to be highlighted in order to be mitigated against. This type of analysis is very useful to an engineer as it allows them to troubleshoot problems quickly. However this analysis only considers the system to be in either two states, fully functioning or failure mode. This is a disadvantage to this type of analysis as it doesn’t help with maintainability, rather looks to solve a problem when a fault occurs.
When assessing the reliability of the system, the reliability block diagram method is useful as it takes into account the probability of each component failing. The accumulation of these component reliabilities, result in a value for the reliability of the system as a whole. With this value determined, the mean time to failure can be found and a graph can be plotted to depict how probability of failure increases alongside the operational time. Again this analysis allows for the Wind Turbines probability of failure to be determined, however no information is given which can aid with maintaining the turbine, only fixing it once its fixed. For this analysis to be useful, a full FTA tree must be constructed with RBD analysis carried out at component level.
Overall the FTA and RBD analysis is useful in predicting the reliability of the wind turbine; however no information or data is given which could help with maintainability. An issue which occurs with all of the analysis is that the data used needs to be reliable to in order to predict the reliability of the wind turbine.
Final statement
The analysis conducted in this report allows the reliability of a wind turbine to be explored in depth and serves as a valuable tool both in design and subsequent operation of the system. Tools employed in determining the numerical values representing reliability heavily rely on accurate and statistically valid data set, which further emphasizes the need for lifetime data collection in all operated turbines. Accurate fault databases allow for these tools to aid both in initial design, and in implementation of maintenance procedures, which serves to improve overall system availability and safety of operation. The end result of this approach is a cheaper, safer and more predictable wind power solution.
Appendix 1 - Table of Meetings Meeting Date
Alistair Lambert
Artur Placha
Benjamin Holmes

Action
Time (Hrs)
Action
Time (Hrs)
Action
Time (Hrs)
13/11/2014
Reliability tutorial
3
Tutorial
3
Tutorial
3
14/11/2014
Own Research
3
Own Research
2
Own Research
2
15/11/2014
None
0
None
0
None
0
16/11/2014
None
0
None
0
None
0
17/11/2014
None
0
None
0
None
0
18/11/2014
Individual work
2
Individual work
3
Individual Work
0
19/11/2014
Tutorial
1
Not present - Design flight test
0
Not present - Design flight test
0
20/11/2014
Tutorial
2
Tutorial
3
Tutorial
3
21/11/2014
Individual work
2
Individual work
2
None
0
22/11/2014
Individual work
1
Individual work
1
Individual Work
0
23/11/2014
Individual work
2
Individual work
2
Individual Work
1
24/11/2014
Group progress meeting
1
Group progress meeting
1
Group progress meeting
1
25/11/2014
Individual work
1
Individual work
1
Individual Work
4
26/11/2014
Tutorial
1
Tutorial
1
Tutorial
1
27/11/2014
Tutorial
3
Tutorial
3
Tutorial
3
28/11/2014
Individual work
3
Individual work
3
Individual Work
5
29/11/2014
Individual work
1
Individual work
1
None
0
30/11/2014
Individual work
2
Individual work
2
None
0
01/12/2014
Individual Work
3
Individual Work
3
Individual Work
3
02/11/2014
Individual Work
6
Individual Work
4
Individual Work
5
03/11/2014
Tutorial & Individual Work
2
Tutorial & Individual Work
6
Tutorial & Individual Work
5
04/12/2014
Tutorial & Individual Work
8
Tutorial & Individual Work
5
Tutorial & Individual Work
9
05/12/2014
Individual Work
7
Individual Work
5
Individual Work
6

54

51

51

Robert Smithers
Simon Hicks
Action
Time (Hrs)
Action
Time (Hrs)
Tutorial
3
Tutorial
3
Own Research
3
Own Research
1
None
0
None
0
None
0
None
0
None
0
None
0
None
0
None
0
Not present - Design flight test
0
Not present - Design flight test
0
Tutorial
2
Tutorial
3
None
0
Individual work
2
Individual work
2
Individual work
1
Individual work
1
None
0
Group progress meeting
1
Group progress meeting
1
Individual work
4
Individual work
2
Tutorial
2
Tutorial
1
Tutorial
3
Tutorial
3
Individual work
5
Individual work
2
None
0
Individual work
3
None
0
None
0
Individual Work
5
Individual Work
3
Individual Work
7
Individual Work
5
Tutorial & Individual Work
2
Tutorial & Individual Work
5
Tutorial & Individual Work
4
Tutorial & Individual Work
6
Individual Work
11
Individual Work
7

55

48
The work was broken down into parts:
Part 1 and 2 - Simon Hicks
Part 3 and 4 - Alistair Lambert
Part 5 - Rob Smithers (help by Artur Placha)
Part 6 and 7 - Ben Holmes
Part 8 and 9 - Artur Placha

With everyone peer reviewing each other’s work and the final conclusion being a collaborative effort and Alistair Lambert as group leader
Appendix 2
System Data
Sub System
Assembly
Contribution n to Total Failure Rate % (failures/turbine/year)
Contribution n to Average Time Lost % (hours/year)
POWER MODULE
FREQUENCY CONVERTER
12.96%
18.39%
POWER MODULE
GENERATOR ASSEMBLY
7.16%
10.47%
POWER MODULE
LV SWITCHGEAR
5.88%
3.03%
POWER MODULE
MV SWITCHGEAR
3.32%
3.27%
POWER MODULE
TRANSFORMER
1.71%
1.84%
POWER MODULE
POWER FEEDER CABLES
0.97%
0.67%
POWER MODULE
UNKNOWN
0.45%
0.30%
POWER MODULE
POWER CABINET
0.12%
0.03%
POWER MODULE
PROTECTION CABINET
0.09%
0.30%
ROTOR MODULE
PITCH SYSTEM
21.29%
23.32%
ROTOR MODULE
BLADES
1.45%
2.13%
ROTOR MODULE
HUB
1.40%
1.84%
ROTOR MODULE
SLIPRINGS
0.43%
0.67%
ROTOR MODULE
HUB COVER
0.05%
0.04%
ROTOR MODULE
UNKNOWN
0.01%
0.01%
ROTOR MODULE
BLADE BEARINGS
0.01%
0.05%
CONTROL&COMMUNICATION SYSTEM
SENSORS
4.06%
4.12%
CONTROL&COMMUNICATION SYSTEM
COMMUNICATION SYSTEM
3.83%
3.41%
CONTROL&COMMUNICATION SYSTEM
SAFETY CHAIN
3.34%
2.21%
CONTROL&COMMUNICATION SYSTEM
CONTROLLER H/W
2.43%
1.44%
CONTROL&COMMUNICATION SYSTEM
CONTROLLER S/W
1.42%
0.62%
CONTROL&COMMUNICATION SYSTEM
HUMAN&OPERATIONAL SAFETY DEVICES
0.23%
0.91%
CONTROL&COMMUNICATION SYSTEM
UNKNOWN
0.22%
0.07%
CONTROL&COMMUNICATION SYSTEM
ANCILLARY EQUIPMENT
0.02%
0.02%
NACELLE MODULE
YAW SYSTEM
11.28%
7.30%
NACELLE MODULE
NACELLE SENSORS
0.29%
0.18%
NACELLE MODULE
NACELLE COVER
0.06%
0.11%
NACELLE MODULE
NACELLE BEDPLATE
0.04%
0.06%
NACELLE MODULE
UNKNOWN
0.01%
0.00%
DRIVETRAIN MODULE
GEARBOX ASSEMBLY
5.13%
4.66%
DRIVETRAIN MODULE
MECHANICAL BRAKE
0.47%
0.33%
DRIVETRAIN MODULE
HIGH SPEED SHAFT TRANSMISSION
0.41%
0.41%
DRIVETRAIN MODULE
MAIN SHAFT
0.29%
1.09%
DRIVETRAIN MODULE
UNKNOWN
0.10%
0.12%
DRIVETRAIN MODULE
GENERATOR SILENT BLOCKS
0.02%
0.08%
AUXILIARYE QUIPMENT
ELECTRICAL PROTECTION&SAFETY DEVICES
1.32%
0.73%
AUXILIARYE QUIPMENT
HYDRAULIC SYSTEM
1.19%
1.42%
AUXILIARYE QUIPMENT
TOP
0.53%
0.44%
AUXILIARYE QUIPMENT
SERVICE CRANE
0.32%
0.15%
AUXILIARYE QUIPMENT
COOLING SYSTEM
0.31%
0.12%
AUXILIARYE QUIPMENT
WTG METEOROLOGICAL STATION
0.30%
0.22%
AUXILIARYE QUIPMENT
LIGHTING AND POWER POINTS
0.18%
0.10%
AUXILIARYE QUIPMENT
GROUND
0.15%
0.09%
AUXILIARYE QUIPMENT
LIGHTNING PROTECTION SYSTEM
0.15%
0.17%
AUXILIARYE QUIPMENT
LIFT
0.12%
0.09%
AUXILIARYE QUIPMENT
GROUNDING
0.11%
0.06%
AUXILIARYE QUIPMENT
ELECTRICAL CABINETS
0.09%
0.05%
AUXILIARYE QUIPMENT
UPS CABINET
0.03%
0.02%
AUXILIARYE QUIPMENT
BEACON
0.02%
0.01%
AUXILIARYE QUIPMENT
UNKNOWN
0.02%
0.03%
AUXILIARYE QUIPMENT
HUB CABINET
0.02%
0.00%
AUXILIARYE QUIPMENT
FIREFIGHTING SYSTEM
0.01%
0.03%
AUXILIARYE QUIPMENT
ELECTRICAL AUXILIARY CABLING
0.01%
0.02%
STRUCTURAL MODULE
TOWER
2.66%
1.75%
STRUCTURAL MODULE
FOUNDATIONS
0.70%
0.37%
WIND FARM
WIND FARM SYSTEM
0.71%
0.27%
WIND FARM
COMMON FACILITIES
0.01%
0.00%
WIND FARM
UNKNOWN
0.01%
0.01%
CONDITION MONITORING SYSTEM
DATA LOGGER
0.06%
0.27%
CONDITION MONITORING SYSTEM
CONDITION SENSORS&CABLES
0.03%
0.07%
CONDITION MONITORING SYSTEM
PROTOCOL ADAPTER CARD FOR DATA LOGGER
0.01%
0.00%
CONDITION MONITORING SYSTEM
SENSORS
0.01%
0.00%

Reliability Analysis
The Weibull Analysis required the use of these tables:

Bibliography
Andrawus, J. A. (2008). Maintenance Optimisation for Wind Turbines. Aberdeen: The Robert Gordon University.
E.ON. (2004). Wind Report. Technical Report.
Farmery, T. (2013, February 25). Wind turbines are beautiful, says head of national trust. Retrieved December 05, 2014, from TheTimes: http://www.thetimes.co.uk/tto/environment/article3698219.ece
Huhn, P. Affected Components and Downtime. ISET.
IPCC. (2013). Climate Change 2013. Cambridge: Cambridge University Press.
Luminosity Engineering Technologies. (n.d.). Wind Turbines. Retrieved December 04, 2014, from luminosityengtech: http://www.luminosityengtech.com/joomla/index.php/applications/wind-turbines
Machine Design. (2001, March 1). Winds of change are blowing for couplings. Retrieved December 04, 2014, from machine design: http://machinedesign.com/technologies/winds-change-are-blowing-couplings
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W. Musial, S. B. (2007). Improving Wind Turbine Gearbox Reliability. Milan: N.R.E.L.

Bibliography: Andrawus, J. A. (2008). Maintenance Optimisation for Wind Turbines. Aberdeen: The Robert Gordon University. E.ON. (2004). Wind Report. Technical Report. Farmery, T. (2013, February 25). Wind turbines are beautiful, says head of national trust. Retrieved December 05, 2014, from TheTimes: http://www.thetimes.co.uk/tto/environment/article3698219.ece Huhn, P. Affected Components and Downtime. ISET. IPCC. (2013). Climate Change 2013. Cambridge: Cambridge University Press. Luminosity Engineering Technologies. (n.d.). Wind Turbines. Retrieved December 04, 2014, from luminosityengtech: http://www.luminosityengtech.com/joomla/index.php/applications/wind-turbines Machine Design. (2001, March 1). Winds of change are blowing for couplings. Retrieved December 04, 2014, from machine design: http://machinedesign.com/technologies/winds-change-are-blowing-couplings Vittrup, C. (2014, January 15). 2013 was a record-setting year for Danish wind power. Retrieved December 05, 2014, from enerninet: http://energinet.dk/EN/El/Nyheder/Sider/2013-var-et-rekordaar-for-dansk-vindkraft.aspx W. Musial, S. B. (2007). Improving Wind Turbine Gearbox Reliability. Milan: N.R.E.L.