1. O&M Modelling for Large Scale Offshore Wind Farms
by Use of Markov Processes
6th of February 2013, EWEA 2013
Remote Wind Farms: Strategies and Concepts
Burcu Özdirik

2. Background Far-shore Wind Farm Projects and Challenges
Various Design Parameters
Number and type of WTG
Distance between WTGs
Wear and tear on the WTG components
Various Locations of Sites
Distance to shore
Weather conditions
Capacity factor
Operational Particulars
Logistic strategies
Working regulations
Scheduled maintenance concepts
Offshore Wind Projects
Triton Knoll
North Hoyle
Gwynt y Môr
Rhyl Flats
Greater Gabbard
Nordsee Ost
Innogy Nordsee 1
Tromp Binnen
Thornton Bank
Projects in operation or under construction
Projects consented or in development
Atlantic Array
Dogger Bank
Galloper
Comparability with regards to the economic efficiency of O&M is required.

3. Technical Availability
Modelling Approach
Modelling Framework
System
Characteristics
System
Accessibility
System
Reliability
Offshore Operations
Markov Modell
assembly - loop
monthly - loop
annual - loop
Technical Availability
Demand of Resources
results

4. Scope of Work Influencing factors of Offshore O&M
Repair Mission
Weather
on site
Respective Period
(e.g. 1 a = 8760 [h]
Weather
waiting-
time
Port Dist.
Downtime due to
Scheduled
Maintenance [h]
Technical Availability
of OWF
Logistics
Repair-time
Downtime [h]
Logistics
Travel-
time
Port Dist.
Downtime due to
Unscheduled
Maintenance [h]
Economic Efficiency
of Operations
Access-
time
Weather
Logistics
Unplanned
Outages / Failures
Component
Repair-
time
Costs
Type of WTG
Type of FC
Lead-
time
Logistics
Spare Parts
Mobil.-
time
Logistics
Personnel

5. means of transportation
Modelling Approach
Elements of System and their characteristics
Technical System
(OWF)
Reliability
Parameters
FC I MC
repair - time
FC II MC
WTG
Assemblies
spare parts
System-Reliability
Characteristics of MC
FC III MC
number of technicians
Markov Modell
Sub-system
means of transportation
FC IV MC
PAX
lead time
lead time MC
BoP
FC I
FC II
FC III
FC IV
Assemblies
System-Reliability
Sub-system
equipment
availability
Speed [kmh]
access-time
Hs or vw limitation
mobilisation time
fuel consumption
FC – Failure Class
MC – Maintenance Class

6. Modelling Approach Assembly-specific Markov-Chain
Initial probability (probability to fail)
Probability of Repair
Monthly accessibility indicator
Duration of mission
Access- and Mobilisation time
Travelling time
Repair time
Further delays
Curtailed
Load
Operation
Probability of subsequent damages
Residence period is determined by the property of ergodicity
Monthly assembly-specific transition matrices
FC I
FC II
FC III
FC IV

7. Results Technical Availability
General Assumptions
Failure data of IWES [1]
Weather data of FINO 1 [2]
Wave height limitations 1.5 m
Wind speed limitation 18 m/s
Service of 180 man-hours per WTG starting in June
97% - Thornton Bank (2009) and alpha ventus 97% (2011) [3, 4, 5]
97%
95%
Offshore Wind Farm
50 x 5 MW WTGs (rotor diameter of 128 m)
40 km distance to an operational port
Area of site is 40 km² with 8D distance between WTGs
25 service technicians
Large Scale Far Shore Wind Farm
200 x 5 MW WTGs (rotor diameter of 128 m)
100 km distance to an operational port
Area of site is 280 km² with 10D distance between WTGs
100 service technicians

8. Results Average Annual Downtime per WTG
Annual Downtime [h/a]
70 h
25 h
60 h

9. Conclusions
Modelling results reflect the current experiences regarding operational offshore wind farms
Flexible assembly-specific modelling over the calendar year enables a mid- and long-term comparability of planned offshore wind farms
Markov chains around assembly specific operations enable the modelling of the interacting influence factors and their impact on the technical availability and the demand of resources
The model further enables evaluations with regards to
Logistic strategies & Simultaneous use of vessels
Impact of limiting resources
Maintenance strategies
The model is expandable in order to provide cost-benefit analysis

10. Thank you for your attention.
Burcu Özdirik
Doctoral Candidate
Operations and Maintenance
Offshore Wind, RWE Innogy
Institute of Environmental
Technology and Energy Economics
Technical University of Hamburg- Harburg 10

11. References
[1] S. Faulstich, M. Durstewitz, B. Hahn, K. Knorr and K. Rohring, “Windenergie Report Deutschland 2008,” Institut für Solare Energieversorgunstechnik (ISET), Kassel, [2] FINO 1, [Online available: , accessed on [3] D. Koenemann, “Erwartungen übertroffen – Ein Rückblick auf das erste Betriebsjahr 2011,” BWK – Das Energie Fachmagazin, Bd. 64, pp , 2012 [4] “alpha ventus – Pressemitteilung 2012,” [Online available: accessed on [5] “Repower Systems – Pressemitteilung 2010,” [Online available: accessed on

12. Results Parameter Variation for Far-shore WF Assumptions
Distance within WF
Serviceability
Lead-
times
Maintainability
Reliability
Speed of vessels
Distance to port

13. Accessibility Indicators FINO 1 Weather Data (7 years)

14. Accessibility Indicators for Hs 1.5m FINO 1 Weather Data (7 years)

15. Results Demand of Vessels for the Far-shore Wind Farm

16. Results Demand of CTVs for the Far-shore Wind Farm
Additional CTVs for scheduled Maintenance

17. Split of WTG into Assemblies
Rotor-system (blade, bearing, hub)
Pitch-system
Yaw-system
Generator
Gearbox
Sensors
Drive Train incl. Bearings
Electrical Control

18. Results Split of Downtime for the Far-shore Wind Farm
Far-shore WF
Far-shore WF (with helicopter)

19. Results Split of Downtime for the Far-shore Wind Farm
Far-shore WF (CTV 1.5m)
Far-shore WF (CTV 2m)

20. Results Split of Downtime for the Offshore Wind Farm
Offshore WF
Offshore WF (with helicopter)

21. Transition Probabilities