challenges and perspectives - gacc midwest and maintenance (o&m) cost sums up to ~1/3 of wind...
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Reliability and maintenance of wind turbines Challenges and perspectives
Dr.-Ing. Katharina Fischer
Fraunhofer Institute for Wind Energy and Energy System Technology
www.exportinitiative.bmwi.de
Outline
Fraunhofer IWES
Background: Cost of energy
Reliability
Maintenance
CMS and SHM
Conclusions
Status quo, challenges, trends
One of 66 Fraunhofer institutes in Germany,
approx. 500 employees
Fraunhofer: leading organization for applied
research in Europe
Strategic Alliance with ForWind and the
German Aerospace Center (DLR):
1/3 yields from
industry orders
1/3
public basic
funding
1/3
third-party
grants
Fraunhofer Institute for Wind Energy and Energy System Technology IWES
Research spectrum: Wind turbine as the
sum of dynamically interacting subsystems
Operation and Maintenance (O&M) cost sums up to ~1/3 of wind turbine life
cycle cost (LCC)
In addition: opportunity cost
due to unavailability
Reliability and availability are among the key factors for COE reduction
Influenced by: (a) Inherent reliability (b) Maintenance
Relevance of reliability and maintenance for the cost of energy
Large portion of O&M cost:
Maintenance cost
(service and repair)
Maintenance
Rent
Other
Operations
Insurance Offshore Onshore (new) Onshore (old)
O&
M c
os
t [€
ce
nt/
kW
h]
*1€ ~ 1.35 US$
So
urc
e: S
vo
bo
da
(2
01
3) *
Past: focus on gearbox
Present weak points according
to reliability study for variable-
speed wind turbines in the
RELIAWIND project
(2008-2011) & operators:
Electronical / electrical
components
Pitch system
(incl. pitch converters)
Root-causes / failure
mechanisms often unknown
Reliability of wind turbines:
Weak points
PITCH SYSTEM 23%
FREQUENCY CONVERTER
18%
YAW SYSTEM 7%
GENERATOR ASSEMBLY
11%
LV SWITCHGEAR 3%
GEARBOX ASSEMBLY
5%
SENSORS 4%
COMMUNICATION SYSTEM
4%
SAFETY CHAIN 2%
MV SWITCHGEAR 3%
TOWER 2% OTHER
18%
Downtime
Data source: RELIAWIND, Wilkinson et al. (2011)
PITCH SYSTEM 21%
FREQUENCY CONVERTER
13%
YAW SYSTEM 11%
GENERATOR ASSEMBLY
7% LV SWITCHGEAR
6%
GEARBOX ASSEMBLY
5%
SENSORS 4%
COMMUNICATION SYSTEM
4%
SAFETY CHAIN 4%
MV SWITCHGEAR 3%
TOWER 3%
OTHER 19%
Failure rates
CONFAIL study: two different wind-turbine models (DFIG, IG with full-power
converter) at offshore and onshore sites, ~150 turbines, low-voltage converters
Challenge: Frequency-converter failure
Root-Cause Analysis: Failure mechanisms and
causes, countermeasures
Failure-data analysis,
correlation with
environmental factors
Operating environment in
the field
Measurements on failed
modules, forensic analysis
So
urc
e: C
ON
FA
IL / C
ha
lme
rs / V
att
en
fall
/ IW
ES
Challenge: Frequency-converter failure (CONFAIL project)
Spatial and seasonal clustering of
failures observed
DFIG vs. Full power-converter
(FPC) turbines: FPC show highest
failure rates
DFIG
FPC
Significant correlation of converter failure
with nearby lightning
Forensic analysis:
high-voltage sparkover
So
urc
e: C
ON
FA
IL / C
ha
lme
rs / V
att
en
fall
/ IW
ES
No indications of the “classical”
converter reliability issues, i.e. bond-
wire or solder damage
„Foreign objects“
inside converter
cabinets in wind
turbines
Challenge: Frequency-converter failure (CONFAIL project)
So
urc
e: C
ON
FA
IL / C
ha
lme
rs / V
att
en
fall
/ IW
ES
Contamination with salt and
corrosion products on driver-board
R&D cluster on reliable power electronics for wind turbines
Project partners:
Fraunhofer IWES, Fraunhofer ISIT
4 universities
22 industry partners
Duration: 2013 – 2016 Budget: 8 M€
Research partners:
Industry partners: Project focus and objectives:
Improving reliability and availability of
frequency converters in wind turbines
Root-cause analysis, countermeasures for
existing and future turbines
System behaviour in dynamic operation
Condition monitoring for electronics
Fault-tolerant generator/converter concepts
Germany onshore: trend towards
low-wind turbines with high
rotor-generator-ratios
Drive train concepts of turbines
installed in Germany in 2012:
59% direct drive
41% geared drive
Generators:
global trend towards PMSG
Converters:
slow shift from low-voltage IGBT to medium-voltage IGCT technology
Technological trends
Data source: IWET
∅ Rotor diameter: 88m
∅ Hub height: 111m
∅ Nom. power: 2.4MW
New rotor concepts: avoid pitching of the complete
blade, react locally through deformable parts /
adjustable flaps (SMART BLADES project 2012-2016)
Predictive control based on LIDAR measurement of
approaching wind-field (LIDAR II project 2010-2013,
LIDAR-buoy 2011-2013, …)
Automated production of rotor blades: improved
quality at >10% reduced cost
(BLADEMAKER project 2012-2017)
Technological trends: R&D topics
So
urc
e: D
LR
, IW
ES
Improving reliability and accelerating time-to-market by realistic testing
Rotor Blade Test Hall up to 90 meters
Testing of design prior to series production
Simulation of 20 year life-spans in a few months
DyNaLab with 10 MW Drive Power
Testing of complete wind turbine nacelles
Assessment and optimization of turbine concepts
Shorter certification process
Support-Structure Test Center
Testing support structure fatigue behaviour
Soil-structure interaction
Operation and Maintenance (O&M) cost sums up to ~1/3 of wind turbine life
cycle cost (LCC)
In addition: opportunity cost
due to unavailability
Reliability and availability are among the key factors for COE reduction
Influenced by: (a) Inherent reliability (b) Maintenance
Relevance of reliability and maintenance for the cost of energy
Large portion of O&M cost:
Maintenance cost
(service and repair)
Maintenance
Rent
Other
Operations
Insurance Offshore Onshore (new) Onshore (old)
O&
M c
os
t [€
ce
nt/
kW
h]
*1€ ~ 1.35 US$
So
urc
e: S
vo
bo
da
(2
01
3) *
Maintenance
Corrective maintenance
Preventive maintenance
Pre-determined maintenance
Condition-based maintenance
Maintenance strategies
according to EN13306
Maintenance: Terminology
e.g.
Condition-monitoring
systems for the drive train
Oil sampling and analysis
for the gearbox
…
e.g.
Exchange of oil filters
Greasing of bearings
Retightening of bolts
…
“Run-to-failure”
strategy
Basic concept: use data-based mathematical models to determine the minimum of
direct costs: labor,
material, administration,… and
Indirect costs resulting
from imperfect
maintenance:
production loss, labor,
material, …
Consideration of complete life cycle
Examples:
Quantitative maintenance optimization
Co
st
Maintenance effort
Optimum levels of
maintenance effort and
cost Direct maintenance cost
Lost revenue due
to unavailability
Total cost
Economical assessment of
maintenance strategies
Interval optimization (e.g. time-
based replacement, inspections)
Maintenance scheduling
Maintenance logistics
Spare-part management
…
Prerequisite for tapping the cost-
reduction potential of quantitative
maintenance optimization
Standardization is crucial:
⟹ RDS-PP, ZEUS, GSP
Joint data pool (WinDPool) to
provide broad data basis
EVW II project (2010-2013):
standardization, implementation
of a RAMS/LCC database
Offshore-WMEP (2012-2015)
Importance of in-depth reliability and maintenance data
Source: IWES
Approx. 2/3 of all wind-turbine operators in
Germany have full-service contracts;
further trend towards full-service (→ prev. maint.)
Background:
Large portion of Enercon turbines
Operator / owner structure:
more than half of installed capacity in private
ownership, only ~10% owned by utilities
OEMs gain importance in the lucrative
maintenance business:
Maintenance market: Situation in Germany
Sources:
IWET, IWES
Market share of wind-turbine OEMs, based on
all turbines in operation in 2012
Service by
turbine OEMs Independent
or company-owned
service providers
Condition monitoring: Status quo
Situation in Germany:
Offshore: CMS requested by certification body
Onshore: CMS is optional but common practice at professional operators
Typical costs: ~11.000 US$ for CMS, <2000 US$/year for monitoring service
Status:
Established:
Not industrially available yet:
So
urc
es: W
ölfe
l, c
mc
Rotor bearing
Low-speed and high-speed shaft
Gearbox: gears and bearing
Generator bearings
• Vibration
monitoring
of
• Oil monitoring gearbox
Yaw bearing
Pitch bearing • CM for
Condition monitoring: Challenges, trends, new approaches
Innovative methods used in other industries not applicable in the complex and
highly dynamic drivetrains of wind turbines
Low rotational speeds (and trend towards even lower)
⟹ displacement measurements e.g. on low-speed bearings
Vibration monitoring of planetary gearboxes (often 2-3 pl. stages)
Subjective severity assessment
Limited detection certainty / false alarms
⟹ additional sources of condition information
⟹ R&D on data processing and diagnosis methods, and…
1st prognosis
2nd prognosis
3rd prognosis
Occurrence of failure
So
urc
es: B
rüe
l &
Kja
er,
Cha
lme
rs / I
WE
S
⟹ Quantitative CMS-based
prognosis of residual
component life
Trend in condition monitoring: Integration of SCADA and CMS
Supervisory control
(Nacelle)
Supervisory control
(Tower base)
Wind park network
So
urc
e: H
örin
g, 8
.2 (
20
13
)
Standard separate CMS:
Challenge: false alarms,
e.g. due to active yaw
Control-integrated CMS:
data acquisition in
un-disturbed states
⟹ earlier, enhanced-
certainty fault detection
at lower cost
Analysis software for both:
Rotor blades
Operational modal analysis
Vibration measurement
Laser
Passive thermography (R&D)
Acoustic emission (R&D)
Onshore foundations
Relative displacement
Offshore support structures and towers
R&D project
Structural Health Monitoring: Status quo, new approaches
Sources: Wölfel, Hermos, IAB, SKF,
Baumer, Bosch Rexroth, Wölfel,
IWES, PAC Samos, InfraTec
Reliability and maintenance are crucial factors for further cost-of-energy reduction
Reliability of electronic / electric components and pitch systems must be
improved ⟹ field-experience based root-cause analysis as starting point
Full-size testing for enhanced reliability and shorter time to market
Quantitative maintenance optimization: high cost-reduction potential,
but: joint standardized reliability and maintenance databases needed
Larger machines and investments: CMS and SHM are gaining importance,
integration with SCADA supports diagnosis and residual-life prognosis
Conclusions
