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O&M Modelling for Large Scale Offshore Wind Farms

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Presentation on theme: "O&M Modelling for Large Scale Offshore Wind Farms"— Presentation transcript:

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: 2012, 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


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