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What’s Hot in University Offshore Renewable Research
1
Peter Tavner Emeritus Professor, Durham University Former President of European Academy of Wind Energy Beginning is easy - Continuing is hard Japanese Proverb
Overview
• Preventing failures, monitoring • Condition Monitoring • A cautionary monitoring tale • About wave & tidal • Conclusions
2
Preventing Failures, Monitoring
3
London Array, Offshore Wind Farm 175 x 3.6 MW WTs, 630 MW
4
5
Wind Turbine monitoring in
context Typical data numbers for a 3.6 MW WT:
SCADA: 400-500 I/O, 25% alarms 75% signals;
SHM: 10-20 I/O; CMS: 10-20 I/O.
SCADA evolved from 1980s-requirement
to measure performance of β –testing onshore Danish Concept WTs;
SHM evolved from 1990s-requirement to meet insurance measurement needs to
prove structural strength; CMS evolved from 2000s-requirement from insurers following stall-regulated
machine gearbox failures.
SCADA, < 0.001 Hz
Continuous signals and alarms
Structural Health
Monitoring, SHM, < 5 Hz Not
continuous
Condition Monitoring,
CM, < 35 Hz
Continuous
Diagnosis, 10 kHz Not continuous
CMS, Vibration, Oil & Electrical
Signals
6 Reference 22
CMS in Context
7
Conventional rotating machine condition monitoring
Vibration accelerometers, proximeters particles in oil
Blade and pitch monitoring
Electrical system monitoring
Gearbox Vibration & Particle Count CMS during a 1.3 MW 2-speed WT Gearbox Bearing Fault
8 Reference 12, 13 & 16
Durham 30 kW Wind Turbine Condition Monitoring Test Rig (WTCMTR)
9 Reference 13
Power CMS during a 30 kW WTCMTR Generator Rotor Asymmetry Fault
10 Reference 13
Gear Vibration CMS during a 30 kW WTCMTR Gear Tooth Fault
11
SBPF= 0.0015e0.0433*PR² = 0.7502
SBPF = 0.0065e0.042*PR² = 0.8808
SBPF = 0.0028e0.0437*PR² = 0.8974
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 10 20 30 40 50 60 70 80 90 100
SBPF [g
P2]
Power [%]
Healthy ToothEarly Stages of Tooth WearMissing Tooth
Reference 18
Gear Vibration CMS during a 750 kW WT Gearbox Gear Tooth Fault
12 Reference 18
SCADA Alarms & Signals
13 Reference 22
Alarm Names Turbine Pitch General Turbine Blade1-3 Emergency Rotor Over-current Rotor-side Inverter Over-temperature Rotor-side Inverter IGBT DC LinkOver-Voltage Grid-side Inverter Over-current Grid-side Inverter Over-temperature Grid-side Inverter IGBT Converter (General) Main Switch Grid Voltage Dip
BypassContactor
SeriesContactor
Double Fed Induction Generator
Rotor-side Inverter
Grid-side Inverter
DC Link
Main Switch
RotorStator
Grid
Crowbar
Main WT Transformer
14
Converter SCADA Alarms 1.67 MW Variable Speed WT
Reference 10
Grid Fault 2
Normalised Cumulative Alarm Duration vs Calendar Time 10 alarms associated with grid fault were chosen
Grid Fault1
Converter SCADA Alarms 1.67 MW Variable Speed WT
Reference 10 15
Pitch General & Blade1-3 Emergency
Converter General
Rotor-side Inverter Over-Current &Rotor-side Inverter Over-temperature
Grid Voltage Dip
Grid-side Inverter Over-current
Main Switch
DC Link Over-voltage
16
Converter SCADA Alarms 1.67 MW Variable Speed WT
Reference 10
17
Alarm Name Turbine Pitch General Turbine Blade1-3 Emergency Pitch Warning General PCP Initiated Emergency Feather Control Blade 1 Saturation Limit Blade 1 Short Circuit Servo Pitch Amplifier (SPA) Fault Blade 1
AC
M
Rectifier Diode Bridge
Series Field
Shunt Field
Pitch Gearbox2 IGBT
Converter/SPADC Bus
Battery (EPU)
Relay
Motor Reversing Switches2 Quardrant Chopper
T set by switching frequency
timer
Encoder
Pitch System SCADA Alarms 1.67 MW Variable Speed WT
Reference 10
Pitch Warning General
Pitch GeneralBlade1-3 Emergency
PCP Initiated Emergency Feather Control
Blade 1 Servo Pitch Amplifier
Fault
Blade 1 Saturation Limit
Blade 1 Short Circuit
18
Pitch System SCADA Alarms 1.67 MW Variable Speed WT
Reference 10
SCADA Alarm Key Performance Indices*(KPI)
• KPIs: – KPI 1, Average Alarm Rate: long term average number of
alarms /10 min – KPI 2, Maximum Alarm Rate: maximum number of alarms /10
min • * Standard
– Alarm systems, a guide to design, management and procurement No. 191 Engineering Equipment and Materials Users Association 1999 ISBN 0 8593 1076 0
19 Reference 10
SCADA 10 min Alarm KPIs from 7 Wind Farms
20
Reference 10
Conclusions Wave & Tidal
21
The Problem: UK Offshore Rounds 2 & 3
• Wind Farms of 100-500 WTs • 400 I/O per WT • 20000 WT I/O per Wind
Farm, excluding substation, cables & connection
• Total Wind Farm I/O > 30000
• Onshore: – 75% of faults cause
5 % of downtime – 25% of faults cause
95% of downtime (Reference 10)
• Offshore this 75% of small faults will be critical
• With the alarm rates encountered onshore Operations will be overloaded
• They will consume O&M time & money 22
Reference 15
Power to Weight Ratios of Wind, Wave & Tidal
23
0.1
1.0
10.0
100.0
Vest
as V
90, W
T
SW
T 3.
6, W
T
SW
T 3.
6, O
WT,
Anh
olt
Vest
as V
90, O
WT,
Ken
tish
Flat
s
Vest
as V
90, O
WT,
Bar
row
MC
T, T
SD
, Sea
gen
Atla
ntis
AR
1000
, TS
D, E
ME
C
Oys
ter,
WE
C, E
ME
C
Pel
amis
, W
EC
, Agu
cado
ura
TE5,
WE
C, L
owes
toft
SW
T 2.
3, F
WT,
Hyw
ind
V80
, FW
T, W
indF
loat
Pow
er to
Wei
ght,
kW/to
nne
Offshore Wind Turbines
Installation £1200/kW
CoE £110/MWh
Onshore Wind Turbines
Installation £650/kW
CoE £85/MWh
Tidal Stream Devices
? Installation £3600/kW
CoE £200/MWh
Wave Energy Converters
? Installation £3600/kW
CoE £250/MWh
Floating Wind Turbines
?
Wave Power
24
Pelamis P2, 750 kW
Wavegen-Limpet, 150 kW
Archimedes Wave Swing, 1 MW
Tidal Power
25
Hammerfest Strom 1000, 1 MW
Atlantis AR1000, 1MW
EvoPod, Currently 10kW
21st Century Tidal Devices
• >50 TSD technologies around the world, few will be viable. • TSDs can be horizontal, vertical turbines or oscillating hydrofoils. • Which is the most reliable architecture ?
26
Types of horizontal axis TSDs [1]
Reference 8
What’s the Predicted Failure Rate
Device 3, 100% power
Device 1, 100% power
Device 2, 50% power
Device 4, 50% power
Device 2, 100% power
Device 4, 100% power
Subsystems (Nss) 26 38 38 46 57 59
0
10
20
30
40
50
60
Tota
l num
ber
of su
bsys
tem
s s p
er d
evic
e (N
ss)
Number of subsystems
27 Reference 8
What’s the Predicted Failure Rate
Device 3, 100% power
Device 1, 100% power
Device 2, 50% power
Device 4, 50% power
Device 2, 100% power
Device 4, 100% power
Subsystems (Nss) 26 38 38 46 57 59 Alternative 1 4.074 4.764 3.483 4.499 6.284 6.770 Alternative 2 4.927 5.691 3.899 4.892 8.568 8.608
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
10
20
30
40
50
60
Tota
l num
ber
of su
bsys
tem
s s p
er d
evic
e (N
ss)
Failure Rate Estimates 1 year operation
T
otal Failure rates per device (Failures/year)
Measured λ working WTs
same size
28 Reference 8
How Many Survive in the Water
29 Reference 8
Reliability Model for TSD 1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Pred
icte
d fa
ilure
freq
uanc
y/de
viee
/yea
r λ T
fi (
Failu
res/
year
)
Subassemblies
TSD 1 critical subassemblies
30 Reference 9
TSD 2 Reliability Model Comparison between predictions & reality
31 Reference 9
Conclusions • WT reliability is improving • Offshore WT reliability is < onshore • Subassemblies with high failure rates are consistent • Downtime or MTTR and cost are also important • Failure rates of subassemblies can improve with time • Offshore availability Ai is worse than onshore • WT experience can be mapped onto Tidal Turbines • Current predicted Tidal Turbine reliabilities are poor • Predicted Wave Device reliabilities will also be poor • Wave & Tidal Device reliabilities need to be improved • We need to concentrate on:
– Introduce redundancy; – Remove or relocate high risk components; – Review reliability during design; – Pre-test components and sub-assemblies before putting them to sea. 32
33
Thank you 1. Polinder, H. van der Pijl, F F A, de Vilder, G J, Tavner, P J (2006) Comparison of direct-drive and geared generator concepts for wind turbines, IEEE Trans Energy
Conversion, 21(3): 725 – 733;
2. Tavner, P J, Edwards, C, Brinkman, A, Spinato, F (2006) Influence of wind speed on wind turbine reliability, Wind Engineering, 30(1):55–72;
3. Ribrant, P J J, Bertling L M (2007) Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005, IEEE Trans Energy Conversion, 22(1): 167–173;
4. Hansen, A D, Hansen, L H (2007) Wind turbine concept market penetration over 10 years (1995–2004), Wind Energy, 10(1):81–97;
5. Tavner, P J, Xiang, J P, Spinato, F (2007) Reliability analysis for wind turbines, Wind Energy, 10(1): 1–18; 6. Spinato, F, Tavner, P J, van Bussel, G J W, Koutoulakos, E (2009) Reliability of wind turbine subassemblies, IET Renew Power Gen, 3(4): 387-401;
7. Arabian-Hoseynabadi, H, Tavner, P J, Oraee, H (2010) Reliability comparison of direct-drive and geared-drive wind turbine concepts, Wind Energy, 13(1): 62-63;
8. Feng, Y, Tavner, P J, Long, H (2010) Early Experiences with UK Round 1 Offshore Wind Farms, Invited Paper, Proceedings of the Institution of Civil Engineers, Energy, 163(4): 167-181;
9. Tavner, P J , Faulstich, S, Hahn, B., van Bussel, G J W (2011) Reliability and availability of wind turbine electrical and electronic components, Invited Paper, EPE Journal, 20(4);
10. Faulstich, S, Hahn, B, Tavner, P J (2011) Wind turbine downtime and its importance for offshore deployment, Wind Energy 14(3): 327-337;
11. Qiu, Y, Feng, Y, Tavner, P J, Richardson, P, Erdos, G, Chen, B D (2012) Wind turbine SCADA alarm analysis for improving reliability, Wind Energy 15 (8), 951-966;
12. Feng, Y, Qiu, Y, Crabtree, C J, Long, H, Tavner, P J (2012) Monitoring wind turbine gearboxes, Wind Energy 16 (5): 728–740; 13. Djurovic, S, Crabtree, C J, Tavner, P J, Smith, A.C (2012) Condition monitoring of wind turbine induction generators with rotor electrical asymmetry, IET Renew. Power
Gener., 6(4): 207 – 216;
14. Tavner, P J, Greenwood, D M, Whittle, M W G, Gindele, R, Faulstich, S, Hahn, B (2012) Study of weather & location effects on wind turbine failure rates, Wind Energy 16(2): 175-187;
15. Tavner, P J (2012) Offshore Wind Turbines-Reliability, Availability & Maintenance, IET Energy;
16. Whittle, M W G, Trevelyan, J, Shin, W, Tavner, P J (2013) Improving wind turbine drive-train bearing reliability through pre-misalignment, Wind Energy; 17. Whittle, M W G, Trevelyan, J, Tavner, P J (2013) Bearing currents in wind turbine generators, Journal of Renewable & Sustainable Energy, 5, 053128;
18. Zappalá, D, Tavner, P J, Crabtree, C. J, Sheng, S (2014) Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis, IET Renew Power Gen, in Press;
19. Zaggout, M, Tavner, P J, Crabtree, C J, Ran, L (2014) Wind turbine doubly-fed induction generator rotor electrical asymmetry detection , IET Renew Power Gen, under review;
20. Chen, B D, Matthews, P C, Tavner, P J (2014) Automated wind turbine pitch faults prognosis based on SCADA data using an a-priori knowledge-based ANFIS, IET Renew Power Gen, in print;
21. Mott Macdonald Report: UK Electricity Generation Costs Update, 2010
22. http://www.supergen-wind.org.uk/dissemination.html Please register to download the following reports:
• Survey of CMS Systems; • Survey of SCADA Systems;
23. Stiesdal, H, Madsen, P H (2005) Design for reliability, European Offshore Wind Conference, Copenhagen.
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