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We create advantage ConMoto Consulting Group GmbH ConMoto Short Study: „Maintenance Strategies for Systems in Wind Power Stations“ Munich, December 2011

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Maintenance Strategies for Systems in Wind Power Stations Short Study Representative survey of wind power station operators Representation of basis data and fault data analysis Derivation of economically optimal maintenance strategies Key findings and potentials of maintenance strategies for systems in wind power stations

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Page 1: Report Maintenance Excellence - Wind Power Stations

We create advantage

ConMoto Consulting Group GmbH ConMoto Short Study: „Maintenance Strategies for Systems in Wind Power Stations“ Munich, December 2011

Page 2: Report Maintenance Excellence - Wind Power Stations

2 ConMoto Consulting Group GmbH We create advantage Maintenance_Excellence_Wind_Power_Stations.ppt

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

Page 3: Report Maintenance Excellence - Wind Power Stations

3 ConMoto Consulting Group GmbH We create advantage Maintenance_Excellence_Wind_Power_Stations.ppt

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 □ Vestas □ GE □ Windworld □ Siemens

□ Nordex □ NEG Micon □ RE Power □ HSW / BWU □ Fuhrländer

Page 4: Report Maintenance Excellence - Wind Power Stations

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Instandhaltungs-Strategie

System

Pitchsystem

Windrichtungs-führung

Rotornabe

Rotorblätter

Zust

ands

orie

ntie

rte

Stra

tegi

e

Ant

rieb

*

Elim

inie

rung

s-st

rate

gie

Einf

ache

R

edun

danz

Str

ateg

ie

Meh

rfac

he

Red

unda

nz S

trat

egie

Perio

disc

he S

trat

egie

nger

e In

terv

alle

Perio

disc

he S

trat

egie

rzer

e In

terv

alle

Sequ

entie

lleSt

rate

gie

Cra

shst

rate

gie

52% 1% 1% 10% 31% 4% 1%

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

72%11% 9% 7%

29% 3% 48% 12% 8%

Methodical Approach – Determination of Target Strategies and Analysis of Maintenance Strategies currently used

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

Fault data analysis and ConMoto best-practice experience and know-how

Analysis of the short study

Target Maintenance Strategies

Maintenance Strategies currently in use Inquiry of maintenance strategies for the main systems of wind power stations:

Input side Output side Electronics & control

systems

Risk and Reliability (R2) based Maintenance Strategies

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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

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2000 2005 2010 2015 2020

Wind Power Stations in Germany – Facts & Figures

Ø Onshore full load hours:** 1.700 - 2.100 h (≙19-24%) Ø Offshore full load hours: 3.800 h (≙ 43,5%) Ø Feed-in compensation*** onshore: 0,075 €/kWh (mean over 20 years) Ø Feed-in compensation *** offshore: 0,13 – 0,15 €/kWh (for the first twelve years)

* 1 GW = 1.000 MW = 1.000.000 kW

** Full load hours = produced energy per year / installed power

*** according to EEG (renewable energies law)

www.windmonitor.iwes.fraunhofer.de

www.wind-energie.de/

www.de.wikipedia.org/wiki/windkraftanlagen

6,1 GW*

18,3 GW

26,8 GW

9.247 21.646 17.323

Number of Wind Power Stations (WPS) in Germany and total installed power

Number of WPS

40,0 GW

Estim

ate

28.000 - 33.000

37.000 - 40.000

54,0 GW

Estim

ate

Trend: WPS > 1,5 MW (Offshore)

Source:

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Gear Box18%

Blades24%

Tower24%

Generator10%

Nascelle Module8%

Hub and Main Shaft6%

Cables & Sensor Systems

3%

Hydraulic System

2%

Yaw System2%

Installation3%

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

* Grid connection, foundation, development, planning, other costs; stated value is applicable for onshore installations only

www.wind-energie.de

Total investment costs: □ 75% Wind power station costs □ 25% Auxiliary investment costs*

Total investment costs per kW installed power □ Onshore: 1.000 to 1.700 €/kW □ Offshore: up to 4.300 €/kW

Ø Annual service and maintenance costs (onshore):

~ 2,6% of the wind turbine costs (turbine and generator) without tower and auxiliary investment costs

Ø Annual operating costs (offshore): 0,02-0,04 €/kWh (thereof 30-50% service and

maintenance costs) Typical investment cost structure of a 1,2

MW Wind Power Station

Source:

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About 2/3 of all Faults result in total Loss of Production or reduced Power Production

Wide Load Range

Vibrations

High Dynamic Loads

Environmental Influences

(Temperature, Salt, Dust)

Cause of Faults and Malfunctions

Impacts of Faults and Malfunctions

Critical Systems Electronics & control systems Blades Pitch system Generator

windmonitor.iwes.fraunhofer.de

General Requirements of a WPS

Source:

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Clear Trend to higher Fault Rates on newer / larger WPS An

nual

Fau

lt R

ate

[Num

ber /

Yea

r]

Installed Power

< 500 kW

500 - 999 kW

> 1.000 kW

Years of Operation

Ø Fault rate large installation: 3,5

Ø Fault rate mid-sized installation: 2

Fault rate almost not related to age of installation

Trend to higher fault rates on large installation (high complexity)

Plant Reliability according to Age and installed Power

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

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

Source:

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Electrical and Control Systems cause the highest Fault Rates Fa

ult R

ate

[Fau

lts p

er Y

ear a

nd W

PS]

Rate of Damage

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

www.windmonitor.iwes.fraunhofer.de

Fau

lt R

ate

[%]

Source:

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

Down Times caused by Component Failures

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

Perc

enta

ge o

f Dow

n Ti

me

(Hou

rs /

Year

) [%

]

Cum

ulat

ed D

own

Tim

e [%

]

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

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Expenditure necessary for the Maintenance of Wind Power Stations

Number of Years between Major Maintenance Expenditure / Replacement

Mai

nten

ance

Exp

endi

ture

/ R

epla

cem

ent I

nves

tmen

t [€/

KW

]

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

Critical Components

Noncritical Components

Medium critical Components

DEWI 2002 Source:

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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

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ConMoto Method: Risk and Reliability (R2) Based Maintenance Strategies – the „Cube“ in Detail

Condition-based Strategy (Service + Inspection measures according to actual condition) - Sequential strategy - Condition based strategy (Online) Redundancy Strategy (Functional redundancy – bypass) - Single redundancy - Multiple redundancy Preventive Strategy (Planned maintenance interventions according to maintenance and inspection plans - low frequency - high frequency Break-down Strategy (Deliberate operation until failure) - Crash - Technical optimization strategy

a b

c d

e f

g h

► Determination of cost-optimal maintenance strategy for each component

5

Impa

ct o

f Fau

lts a

nd M

alfu

nctio

ns -

y

3

5

3

5

3

1

a

b

e f

c

d

g h

low

hi

gh

1

Replacement Value - x

low high

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Results of Risk and Reliability based Maintenance Strategies

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

Reduced direct costs

Less unplanned breakdown intervention

Improved preventive maintenance

Autonomous maintenance

Technical improvements

Optimized contractor management

Improved plant availability

Improved perfor-mance rate

Improved process stability

Less scrap and rework

Balanced score card optimized budget planning and day-to-day resource management on all hierarchy levels

Direct Maintenance

Costs Losses in O.E.E.

Cos

ts

Maintenance Effort/Expenditure

Post optimization

Post optimization

Direct costs prior to optimization

O.E.E. losses prior to optimizations

-

Minimum

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MaintenanceStrategy

System

Pitch System

Yaw System

Hub

Blades

Con

ditio

n-ba

sed

Stra

tegy

Inpu

t Sid

e*

Tech

nica

l op

timiz

atio

n

St

rate

gy

Sing

le R

edun

danc

y St

rate

gy

Mul

tiple

Red

unda

ncy

Stra

tegy

Plan

ned

prev

entiv

e M

aint

enan

ce lo

w

Freq

uenc

y

Plan

ned

prev

entiv

e M

aint

enan

ce h

igh

Freq

uenc

y

Sequ

entia

l Str

ateg

y

Bre

ak-d

own

Stra

tegy

52% 1% 1% 10% 31% 4% 1%

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

72%11% 9% 7%

29% 3% 48% 12% 8%

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

* Values rounded

1

2

3

4

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Maintenance Excellence by applying Risk and Reliability based Maintenance Strategies to a Wind Power Station – “Input Side” (Current vs. Target Strategies)

Wind Power Station Target Values (x/y/z)

Pitch System (5/5/5)

Yaw System (3/4/3)

Hub (3/1/3)

Blades (5/1/2)

5

Impa

ct o

f Fau

lts a

nd M

alfu

nctio

ns -

y

3

5

3

5

3

1

a

b

e f

c

d

g h

low

hi

gh

1

Replacement Value - x

low high

2

1

3 4

1 52%

1 31%

2 51%

2 34%

3 72%

4 29%

4 48%

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

1 2 3 4

Three sub-systems of the “Drive System" are not operated with the optimum maintenance strategy

Only for the sub-system “Blades" about 50% of the WPS operators apply the strategy “Planned Preventive Maintenance - low Frequency (cost-optimal maintenance strategy for this sub-system)

%

%

%

Target Strategy

Current Strategies (Ø of all participating WPS Operators)*

Significant Deviation from Target Strategy

Medium Deviation from Target Strategy No Deviation; Current = Target Strategy

Key

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MaintenanceStrategy

System

Drive Train

Gear Box

Mechanical Brake

Hydraulic System

Plan

ned

prev

entiv

e M

aint

enan

ce lo

w

Freq

uenc

y

Con

ditio

n-ba

sed

Stra

tegy

Out

put S

ide*

Tech

nica

l op

timiz

atio

n

St

rate

gy

Sing

le R

edun

danc

y St

rate

gy

Mul

tiple

Red

unda

ncy

Stra

tegy

Plan

ned

prev

entiv

e M

aint

enan

ce h

igh

Freq

uenc

y

Sequ

entia

l Str

ateg

y

Bre

ak-d

own

Stra

tegy

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

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

51% 1% 11%36%

1% 1%

76%3% 12% 7% 1%

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

* Values rounded

5

6

7

8

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Maintenance Excellence by applying Risk and Reliability based Maintenance Strategies to a Wind Power Station – “Output Side” Current vs. Target Strategies)

5

Impa

ct o

f Fau

lts a

nd M

alfu

nctio

ns -

y

3

5

3

5

3

1

a

b

e f

c

d

g h

low

hi

gh

1

Replacement Value - x

low high

5 6

7 8

5 30%

5 31%

6 33%

6 28% 7

51%

7 36%

8 76%

High scattering of maintenance strategies used for the sub-systems „Drive Train“ and „Gear Box“

Sub-system „Gear Box“ shows the most significant deviation from the target strategy

About 50% of the WPS operators apply the optimum maintenance strategy to the sub-system “Mechanical Brakes”

%

%

%

Target Strategy

Current Strategies (Ø of all participating WPS Operators)*

Significant Deviation from Target Strategy

Medium Deviation from Target Strategy No Deviation; Current = Target Strategy

Key

Wind Power Station Target Values (x/y/z)

Drive Train (2/2/1)

Gear Box (4/2/2)

Mechanical Brake (1/1/4)

Hydraulic System (3/1/2)

5 6 7 8

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

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MaintenanceStrategy

System

Electrical Systems

Control Systems

Generator

Sensors

Sequ

entia

l Str

ateg

y

Con

ditio

n-ba

sed

Stra

tegy

Elec

tron

ics

& C

ontr

ol S

yste

ms*

Bre

ak-d

own

Stra

tegy

Tech

nica

l op

timiz

atio

n

St

rate

gy

Sing

le R

edun

danc

y St

rate

gy

Mul

tiple

Red

unda

ncy

Stra

tegy

Plan

ned

prev

entiv

e M

aint

enan

ce h

igh

Freq

uenc

y

Plan

ned

prev

entiv

e M

aint

enan

ce lo

w

Freq

uenc

y

83%1% 6% 8% 1%

53% 1% 31% 8% 6% 1%

28%3% 1% 13% 22%

33%

82%1% 5% 2% 8% 1%

Results of the Short Study – Maintenance Strategies for Components of the “Electronics & Control Systems” (currently used Strategies)

* Values rounded

9

10

11

12

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Maintenance Excellence by applying Risk and Reliability based Maintenance Strategies to a Wind Power Station – “Electronics & Control Systems” (Current vs. Target Strategies)

5

Impa

ct o

f Fau

lts a

nd M

alfu

nctio

ns -

y

3

5

3

5

3

1

a

b

e f

c

d

g h

low

hi

gh

1

Replacement Value - x

low high

11

12

10

9

9 83%

10 53%

10 31%

11 33%

11 28%

12 82%

All sub-systems of the „Electronic & Control Systems“ show significant deviations from the optimum strategies

Sub-system „Electrical Systems“ and “Control Systems” shows the most significant deviation from the cost-optimal maintenance strategy

%

%

%

Target Strategy

Current Strategies (Ø of all participating WPS Operators)*

Significant Deviation from Target Strategy

Medium Deviation from Target Strategy No Deviation; Current = Target Strategy

Key

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

Wind Power Station Target Values (x/y/z)

Electrical Systems (4/5/5)

Control Systems (4/4/5)

Generator (4/5/2)

Sensors (4/1/4)

9 10 11 12

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Key Findings of the ConMoto Short Study „Maintenance Strategies for Systems in Wind Power Stations“

More than 75% of the participating WPS operators apply to half of all evaluated sub-systems maintenance strategies that deviate significantly from the optimum strategies

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

Pitch System Yaw System Drive Train Electrical Systems Control Systems Sensors

More than 30% of the participating WPS operators apply partly optimum maintenance strategies to 1/4 of all evaluated sub systems

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

Blades Mechanical Brake Generator

Hub Gear Box Hydraulic System

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What does this mean for Onshore Wind Power Stations?

35 x WPS total installed power: 52,5 MW Costs per installed kilowatt: € 1.100 Total investment: ~ 58 Mio.€

Annual service and maintenance costs: 848 k€ (2,6% of the WPS investment costs, w/o tower, w/o auxiliary investment costs)

Reduction of direct maintenance costs by 15%

Cost savings: 127 k€/a

Example 1: Onshore Wind Farm with 35 Wind Power Stations

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

Ø Fault rate large WPS: 3,5 per year Ø Down time per fault / malfunction: 6 days Annual loss of feed-in compensation caused by

down time: 432 k€

Reduction of fault rate to 2/year

Cost savings: 185 k€/a

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12 × 5 MW WPS total installed power: 60 MW Total investment: 250 Mio.€ Specific investment costs: 4.100 €/kW

Assumption full load hours: 3.800 h/a Produced energy / year (theoretical): 228.000 MWh Annual operating costs (offshore): 0,03 €/kWh Estimated annual service and maintenance

costs: 2,7 Mio.€ (40% of operating costs)

Reduction of direct maintenance costs by 15%

Cost savings: 405 k€/a

Example 2: Offshore Wind Farm with 12 Wind Power Stations

Produced energy / day (theoretical): 626 MWh Ø Feed-in compensation acc. to EEG: 140 €/MWh

Ø Fault rate large WPS: 3,5 per year Ø Down time per fault / malfunction: 6 days Annual loss of feed-in compensation caused by

down time: 1,8 Mio.€

Reduction of fault rate to 2/year

Cost savings: 700 k€/a

What does this mean for Offshore Wind Power Stations?

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Projection of annual Savings Potential for Wind Power Stations in Germany

* Assumption: 1.100 € / kW installed power

** Assumption : 1,5% of total investment (per annum)

*** Realizable through implementation of Maintenance Excellence findings and know-how

www.wind-energie.de/

► 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:

2011 22.000 28.000 30.800 462

2015 28.000 - 33.000 40.000 44.000 660

2020 37.000 - 40.000 54.000 59.400 891 141 - 211

104 - 156

73 - 109

89 - 134

66 - 99

46 - 69

Total Wind Power Stations Germany

Year1 Number of

WPS1

Installed Power1

[MW]

Total Invest.* [Mio. €]

Maintenance Costs**

[Mio. € / a]

Potential Savings*** Maint. Costs [Mio. € / a]

Down Time Costs [Mio. € / a]

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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

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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.

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ConMoto Consulting Group

LEAN Excellence

Innovation Excellence

Lean Production Lean Administration Lean Value Chains Lean Development Lean Enterprise Lean Transformation Working Capital Management

Value oriented Maintenance and Asset Innovation

Risk and Reliability based Maintenance Strategies

Time Management, Scheduling, Capacity Planning

Key Performance Indicators Spare Part Management Contractor Management Value Engineering

Procurement Excellence

Lean Structures and Processes

Significant Price Reductions for goods and services to be produced

Supply Network Management Qualification Offensive

„QAMPUS“

Product Clinic Comprehensive Method Know-

how (QFD, FMEA, TRIZ) Technology Evaluation Business Area and Launch

Strategies Implementation of Product-/

Process Innovation

Value oriented Enterprise Development

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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: [email protected] ConMoto Consulting Group GmbH Boschetsrieder Str. 69 81379 Munich, Germany

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