What’s Hot in University Offshore Renewable Research · 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:

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


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


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 2, 50% power

Device 4, 50% 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 2, 50% power

Device 4, 50% 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.

Documents

What’s Hot in University Offshore Renewable Research · 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: