Report Maintenance Excellence - Wind Power Stations

ConMoto Consulting Group GmbH

ConMoto Short Study:
„Maintenance Strategies for Systems in
Wind Power Stations“

Munich, December 2011

We create advantage

Content

1. Basic Concept and Objectives of the Short Study
„Maintenance Strategies for Systems in Wind Power Stations“

2. Wind Power Stations – Basic Data and Analysis of Fault Data

3. Cost-optimal Maintenance Strategies for Wind Power Stations

4. ConMoto Consulting Group – Facts & Figures

 ConMoto Consulting Group GmbH We create advantage Maintenance_Excellence_Wind_Power_Stations.ppt 2

ConMoto Short Study „Maintenance Strategies for Systems in Wind
Power Stations “ (2011) – Scope and Basic Data

Evaluation of Basic Data

 Representative survey of wind power station
operators

 Ø Full load hours per year: 2.141 h (Onshore)
 Ø Full load hours per year : 3.695 h (Offshore)

 Evaluated manufacturers :
□ Enercon □ Nordex
□ Vestas □ NEG Micon
□ GE □ RE Power
□ Windworld □ HSW / BWU
□ Siemens □ Fuhrländer


Methodical Approach – Determination of Target Strategies and Analysis
of Maintenance Strategies currently used
Fault data analysis and ConMoto best-practice Target Maintenance
experience and know-how Strategies
Risk and
Reliability (R2)
based
Maintenance
Strategies

Analysis of the short study Maintenance Strategies currently in use

Periodische Strategie

Periodische Strategie
Redundanz Strategie

Redundanz Strategie

Zustandsorientierte
Instandhaltungs-

Inquiry of maintenance strategies

kürzere Intervalle
längere Intervalle
Strategie

Crashstrategie

Eliminierungs-

Sequentielle
Mehrfache

Strategie

Strategie
strategie

Einfache
for the main systems of wind System

power stations: Pitchsystem
52% 1% 1% 10% 31% 4% 1%

Windrichtungs-

 Input side führung

Antrieb*
51% 3% 1% 10% 1%
34%

 Output side Rotornabe
72%
11% 9% 7%

 Electronics & control Rotorblätter
29% 3% 48% 12% 8%

systems
► Value creation areas and required actions can be derived from comparison of target vs. currently
used Maintenance Strategies. The goal is Maintenance Excellence

Content






Wind Power Stations in Germany – Facts & Figures

54,0 GW
 Ø Onshore full load hours:**
1.700 - 2.100 h (≙19-24%)
40,0 GW

 Ø Offshore full load hours:

Estimate

Estimate
26,8 GW 3.800 h (≙ 43,5%)
Number of
WPS
18,3 GW
 Ø Feed-in compensation*** onshore:
0,075 €/kWh (mean over 20 years)
6,1 GW* 28.000 - 37.000 -
9.247 17.323 21.646 33.000 40.000  Ø Feed-in compensation *** offshore:
0,13 – 0,15 €/kWh (for the first twelve years)
2000 2005 2010 2015 2020
Number of Wind Power Stations (WPS) in Germany
and total installed power
Trend: WPS
> 1,5 MW (Offshore)


Source: www.windmonitor.iwes.fraunhofer.de * 1 GW = 1.000 MW = 1.000.000 kW
www.wind-energie.de/ ** Full load hours = produced energy per year / installed power
www.de.wikipedia.org/wiki/windkraftanlagen *** according to EEG (renewable energies law)

Blades and Tower are Main Cost Drivers of a Wind Power Station

 Total investment costs:
□ 75% Wind power station costs
Hydraulic Yaw System □ 25% Auxiliary investment costs*
Cables & Sensor
System 2%
Systems
3%
2% Installation  Total investment costs per kW installed power
3% □ Onshore: 1.000 to 1.700 €/kW
Hub and Main Gear Box
Shaft 18% □ Offshore: up to 4.300 €/kW
6%
 Ø Annual service and maintenance costs
Nascelle Module
8% (onshore):
~ 2,6% of the wind turbine costs (turbine and
Blades
24% generator) without tower and auxiliary
Generator
10% Tower investment costs
24%
 Ø Annual operating costs (offshore):
0,02-0,04 €/kWh (thereof 30-50% service and
Typical investment cost structure of a 1,2
maintenance costs)
MW Wind Power Station


Source: www.wind-energie.de * Grid connection, foundation, development, planning,
other costs; stated value is applicable for onshore
installations only

About 2/3 of all Faults result in total Loss of Production or reduced
Power Production
General Requirements of a WPS Cause of Faults and Malfunctions

Wide Load Range

Vibrations

High Dynamic
Loads

Environmental
Influences Impacts of Faults and Malfunctions
(Temperature, Salt, Dust)

Critical Systems
 Electronics & control systems
 Blades
 Pitch system
 Generator


Source: windmonitor.iwes.fraunhofer.de

Clear Trend to higher Fault Rates on newer / larger WPS

Plant Reliability according to Age and installed Power

Installed Power  Ø Fault rate large
< 500 kW installation: 3,5
500 - 999 kW
 Ø Fault rate mid-sized
Annual Fault Rate

> 1.000 kW
installation: 2
[Number / Year]

 Fault rate almost not
related to age of
installation
 Trend to higher fault
rates on large
installation (high
complexity)

Years of Operation

► Every fault / malfunction leads to an average down time of 6 days

Source: Hahn,B.; Durstewitz M.; Rohrig K.: Reliability of Wind Turbines; Institut für Solare Energieversorgungstechnik (ISET)
Verein an der Universität Kassel e.V., 34119 Kassel, Germany

Electrical and Control Systems cause the highest Fault Rates

Rate of Damage
[Faults per Year and WPS]

Fault Rate [%]
Fault Rate

► Mechatronic components (e.g. sensors) as well as hydraulic systems are the
second most common causes for down times

Source: www.windmonitor.iwes.fraunhofer.de

More than 39% of the annual Down Time is caused by the Pitch System
and the Electrical System

Down Times caused by Component Failures

Cumulated Down Time [%]
Percentage of Down Time
(Hours / Year) [%]

► Five sub-systems (pitch system, electrical systems, generator, control systems, yaw
system) cause 71% of the total annual down time

Source: http://www.winergy-group.com

Expenditure necessary for the Maintenance of Wind Power Stations
Maintenance Expenditure / Replacement Investment [€/KW]

Replacement investment ca. 54% of
the WPS total investment (20 years)

Critical
Components

Noncritical
Components

Medium
critical
Components

Number of Years between Major Maintenance Expenditure / Replacement


Source: DEWI 2002

Content






ConMoto Method: Risk and Reliability (R2) Based Maintenance
Strategies – the „Cube“ in Detail
Condition-based Strategy
(Service + Inspection measures
according to actual condition)
g - Sequential strategy
f h
h - Condition based strategy
e g
5 (Online)
Impact of Faults and Malfunctions - y

Redundancy Strategy
high

(Functional redundancy – bypass)
e - Single redundancy
f - Multiple redundancy
3
d Preventive Strategy
b
(Planned maintenance interventions
low

c according to maintenance and
a 5 inspection plans
3 c - low frequency
d - high frequency
1 3 5 1
Break-down Strategy
low high (Deliberate operation until failure)
a - Crash
Replacement Value - x
b - Technical optimization strategy

► Determination of cost-optimal maintenance strategy for each component

Results of Risk and Reliability based Maintenance Strategies

Direct
Maintenance Losses in O.E.E.
Costs

Costs
 Reduced direct Direct costs  Improved plant
costs Minimum
prior to availability
 Less unplanned
optimization  Improved perfor-
breakdown
intervention mance rate
 Improved  Improved process
Post optimization
preventive stability
maintenance O.E.E. losses
-  Less scrap and
 Autonomous prior to rework
maintenance
optimizations  …
 Technical
improvements Post optimization
 Optimized
contractor Maintenance Effort/Expenditure
management
 …
 Balanced score card optimized budget planning and day-to-day
resource management on all hierarchy levels

► Reduction of maintenance costs and simultaneous decrease of production losses combined
with optimal budget and resources control lead to optimum total costs

Results of the Short Study – Maintenance Strategies for Components of
the „Input Side“ (currently used Strategies)

Multiple Redundancy
Break-down Strategy

Sequential Strategy
Single Redundancy

Planned preventive

Planned preventive
Maintenance

Maintenance high
Maintenance low

Condition-based
Strategy

optimization

Frequency

Frequency
Technical

Strategy

Strategy

Strategy

Strategy
System

1 Pitch System
52% 1% 1% 10% 31% 4% 1%

2 Yaw System
Input Side*

51% 3% 1% 10% 1%
34%

3 Hub
72%
11% 9% 7%

4 Blades
29% 3% 48% 12% 8%


* Values rounded

Maintenance Excellence by applying Risk and Reliability based
Maintenance Strategies to a Wind Power Station – “Input Side”
(Current vs. Target Strategies)
Key  Three sub-systems of the
“Drive System" are not
1 Target Strategy
operated with the optimum
f h Current Strategies (Ø of all
maintenance strategy
g participating WPS Operators)*
5 e

Significant Deviation  Only for the sub-system
2 % from Target Strategy “Blades" about 50% of the
high

Medium Deviation from WPS operators apply the
% Target Strategy strategy “Planned Preventive
No Deviation; Maintenance - low Frequency
% Current = Target (cost-optimal maintenance
3 Strategy
1 2 d strategy for this sub-system)
b 31% 34%
1 2 4
low

52% 51%
48% c
a 3 4 5
72% 29% 3 4 3 Wind Power Station Target Values (x/y/z)
1
1 3 5 1 Pitch System (5/5/5)
low high 2 Yaw System (3/4/3)
3 Hub (3/1/3)
4 Blades (5/1/2)


* Only the two most commonly used maintenance strategies are
displayed in the cube; if value is >70% only the main strategy is listed

the “Output Side” (currently used Strategies)

Multiple Redundancy
Break-down Strategy

Sequential Strategy
Single Redundancy

Planned preventive

Planned preventive
Maintenance

Maintenance high
Maintenance low

Condition-based
Strategy

optimization

Frequency

Frequency
Technical

Strategy

Strategy

Strategy

Strategy
System

5 Drive Train
30% 3% 1% 9% 5% 31%
22%

6 Gear Box
Output Side*

28% 3% 1% 13% 33%
22%

7 Mechanical Brake 36%
51% 1% 11% 1% 1%

8 Hydraulic System
76%
3% 12% 7% 1%


* Values rounded

Maintenance Strategies to a Wind Power Station – “Output Side”
Current vs. Target Strategies)
Key  High scattering of maintenance
strategies used for the sub-
Target Strategy
systems „Drive Train“ and „Gear
Box“
g participating WPS Operators)*
5 e
 Sub-system „Gear Box“ shows

5 6 Significant Deviation
31% 33% % from Target Strategy the most significant deviation
high

Medium Deviation from from the target strategy
% Target Strategy
 About 50% of the WPS
No Deviation;
% Current = Target operators apply the optimum
3 7 Strategy maintenance strategy to the
5 6 36% d sub-system “Mechanical
b 30% 28% 6 Brakes”
low

7
5 c
a 7 8 51% 5
76% 8 3 Wind Power Station Target Values (x/y/z)
1
1 3 5 5 Drive Train (2/2/1)
low high 6 Gear Box (4/2/2)
7 Mechanical Brake (1/1/4)
8 Hydraulic System (3/1/2)



the “Electronics & Control Systems” (currently used Strategies)

Multiple Redundancy
Break-down Strategy

Sequential Strategy
Single Redundancy

Planned preventive

Planned preventive
Maintenance

Maintenance high
Maintenance low

Condition-based
Strategy

optimization

Frequency

Frequency
Technical

Strategy

Strategy

Strategy

Strategy
System

9 Electrical Systems 83%
Electronics & Control Systems*

1% 6% 8% 1%

10 Control Systems
53% 1% 31% 8% 6% 1%

11 Generator 28% 22%
13%
3% 1% 33%

12 Sensors 82%
1% 5% 2% 8% 1%


* Values rounded

Maintenance Strategies to a Wind Power Station – “Electronics & Control
Systems” (Current vs. Target Strategies)
Key  All sub-systems of the
„Electronic & Control Systems“
9 Target Strategy
show significant deviations
from the optimum strategies
11 11 participating WPS Operators)*
5 e g

33%
Significant Deviation  Sub-system „Electrical
10 % from Target Strategy Systems“ and “Control
high

10 Medium Deviation from Systems” shows the most
31% % Target Strategy significant deviation from the
No Deviation; cost-optimal maintenance
% Current = Target strategy
3 Strategy
b d
9 10
83% 53%
low

c
a 11 12 12 5
28% 82%
3 Wind Power Station Target Values (x/y/z)
1
1 3 5 9 Electrical Systems (4/5/5)
low high 10 Control Systems (4/4/5)

11 Generator (4/5/2)
12 Sensors (4/1/4)



Key Findings of the ConMoto Short Study „Maintenance Strategies for
Systems in Wind Power Stations“
 Pitch System
 More than 75% of the participating WPS operators  Yaw System
apply to half of all evaluated sub-systems  Drive Train
maintenance strategies that deviate significantly from  Electrical Systems
 Control Systems
the optimum strategies
 Sensors

 More than 30% of the participating WPS operators  Hub
apply partly optimum maintenance strategies to 1/4 of  Gear Box
 Hydraulic System
all evaluated sub systems

 Almost 50% of the participating WPS operators apply  Blades
the near optimum / optimum maintenance strategies  Mechanical Brake
 Generator
to 1/4 of all evaluated systems

► Down times and maintenance costs can be significantly reduced by
systematically implementing cost-optimal maintenance strategies

What does this mean for Onshore Wind Power Stations?

Example 1: Onshore Wind Farm with 35 Wind Power Stations

 35 x WPS total installed power: 52,5 MW
 Costs per installed kilowatt: € 1.100
Reduction of direct maintenance
 Total investment: ~ 58 Mio.€ costs by 15%
 Annual service and maintenance costs: 848 k€  Cost savings: 127 k€/a
(2,6% of the WPS investment costs, w/o tower,
w/o auxiliary investment costs)

 Assumption full load hours : 1.900 h/a
 Produced energy / year (theoretical): 99.750 MWh
 Produced energy / day (theoretical): 274 MWh
 Ø Feed-in compensation acc. to EEG: 75 €/MWh Reduction of fault rate to 2/year
 Ø Fault rate large WPS: 3,5 per year Cost savings: 185 k€/a
 Ø Down time per fault / malfunction: 6 days
 Annual loss of feed-in compensation caused by
down time: 432 k€


What does this mean for Offshore Wind Power Stations?

Example 2: Offshore Wind Farm with 12 Wind Power Stations

 12 × 5 MW WPS total installed power: 60 MW
 Total investment: 250 Mio.€
 Specific investment costs: 4.100 €/kW
Reduction of direct maintenance
 Assumption full load hours: 3.800 h/a costs by 15%
 Produced energy / year (theoretical): 228.000 MWh
 Cost savings: 405 k€/a
 Annual operating costs (offshore): 0,03 €/kWh
 Estimated annual service and maintenance
costs: 2,7 Mio.€ (40% of operating costs)

 Produced energy / day (theoretical): 626 MWh
 Ø Feed-in compensation acc. to EEG: 140 €/MWh
Reduction of fault rate to 2/year
 Ø Fault rate large WPS: 3,5 per year
 Ø Down time per fault / malfunction: 6 days  Cost savings: 700 k€/a
 Annual loss of feed-in compensation caused by
down time: 1,8 Mio.€


Projection of annual Savings Potential for Wind Power Stations in Germany

Total Wind Power Stations Germany

Installed Maintenance Potential Savings***
Number of Total Invest.*
Year1 Power1 Costs** Maint. Costs Down Time Costs
WPS1 [Mio. €]
[MW] [Mio. € / a] [Mio. € / a] [Mio. € / a]

2011 22.000 28.000 30.800 462 46 - 69 73 - 109

2015 28.000 - 33.000 40.000 44.000 660 66 - 99 104 - 156

2020 37.000 - 40.000 54.000 59.400 891 89 - 134 141 - 211

* Assumption: 1.100 € / kW installed power
** Assumption : 1,5% of total investment (per annum)
*** Realizable through implementation of Maintenance Excellence findings and know-how

► The consistent use of cost-optimal maintenance strategies could save up to 100 mio.€
in maintenance costs in the next few years and up to 200 mio.€ per year in down time
costs (loss of production)

1) Source: www.wind-energie.de/

Content






ConMoto basic information
 20 years of experience
The ConMoto Consulting Group has been supporting companies to secure and improve their competitiveness
and sustainability for more than 20 years.
 International expertise
Around 80 consultants, distributed across our offices in Munich, Stuttgart, Vienna, St. Gallen, Bratislava, Abu
Dhabi and Shanghai, work expertly and with commitment to realize the best possible benefits for our clients.
 Implementation strength
Our consultants’ high qualifications, supplemented by many years of professional experience, guarantee the
pronounced implementation skill that is necessary to realize the solution concepts we develop together with
our clients.
 Sustainability
Efficient structures and processes, strong innovation skills, effective management and sustainably mobilizing
staff are the project targets pursued within the context of a pioneering strategy.
 Practical orientation
Years of experience enable us to implement innovative concepts with the knowledge of what is feasible
together with our clients.
 We justify your confidence
“We create advantage“ is the guiding principle of our successful and implementation oriented approach.


ConMoto Consulting Group

Value oriented Enterprise Development
LEAN Excellence Value oriented Procurement Innovation
Maintenance and Excellence Excellence
 Lean Production
Asset Innovation
 Lean Administration  Lean Structures and  Product Clinic
 Risk and Reliability based Processes  Comprehensive Method Know-
 Lean Value Chains Maintenance Strategies
 Significant Price Reductions how (QFD, FMEA, TRIZ)
 Lean Development  Time Management, for goods and services to be  Technology Evaluation
 Lean Enterprise Scheduling, Capacity Planning  Business Area and Launch
produced
 Lean Transformation  Key Performance Indicators Strategies
 Supply Network Management
 Working Capital Management  Spare Part Management  Implementation of Product-/
 Qualification Offensive Process Innovation
 Contractor Management „QAMPUS“
 Value Engineering


Your Contact

Dipl.-Ing., MBA Nils Blechschmidt
Senior Partner & Executive Director for the
Consulting Areas Maintenance Excellence and
Asset Innovation
Tel.: +49 (0)89 780 66 - 114
Fax: +49 (0)89 780 66 - 100
E-Mail: Blechschmidt@conmoto.de
ConMoto Consulting Group GmbH
Boschetsrieder Str. 69
 ConMoto Consulting Group GmbH 81379 Munich, Germany
We create advantage Maintenance_Excellence_Wind_Power_Stations.ppt 29

Report Maintenance Excellence - Wind Power Stations

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Report Maintenance Excellence - Wind Power Stations