Dataset for European Installed Offshore Wind Turbines (until year end 2017)
datasetposted on 11.06.2018, 08:18 by Debora Cevasco, Maurizio Collu
Introduction and aim
This dataset is aimed to list and collect the main characteristics of the European Offshore Wind Farms (to end of 2017).
Firstly, this work wants to update and extend the one started by Zhang et al. , who gathered the main information and identified the drivetrain types for some of offshore EU wind turbines’ installed, until the end of 2011.
Secondly, the wind turbines belonging to the population studied by Carroll et al. ,  (in their reliability database), are identified and analysed more in details.
The dataset is organised in an Excel worksheet, consisting of:
sheet 1 - “Legend”
Acronyms and colour coded legend are explained. Additionally the following acronyms are used in the Excel work and throughout this introduction:
- WT(s) = Wind Turbine(s)
- WF(s) = Wind Farm(s)
sheet 2 - "EU WFs”
Data from Zhang et al.  have been verified and updated by accessing the main information of the wind farms (see link in reference in the section).
In particular, for each project, the following information are reported:
- WF name, capacity and country
- number of WTs
- WTs manufacturer/type
- type of control, gearbox, generator, and converter
- year when WF was online
- average distance from shore
- current status of the WF
sheet 3 - "EU WFs (Fully-Grid Connected)”
The fully-grid connected, and still in operation, wind farms are selected out of the ones listed in sheet 1.
In the main table (Range(“A1:N83”)), the WTs are identified in the four drivetrain types (and type D sub-types), defined by Perez et al.  (N2:N83).
A table reporting the acronyms for the “if” cycle on the WT characteristics (speed, gearbox and generator) is reported in cells Range(“AH2:AL11”).
Based on this latter, cells in Range(“Q1:AD84”) contain “if” cycles for identifying the share of each drivetrain type on the total installed capacity. The results are plotted in a pie chart, gathering type A and B.
Finally, the table in Range(“AS1:CA86”) wants to verify how much of the actual installed (fully-grid connected) capacity is accounted in this dataset. WindEurope report on offshore wind energy statistics, to the end of 2017 , is used as a reference, and the sharing to the total capacity of the several manufacturers and of the top 5 countries and is checked.
sheet 4 - “Strath. Stats (population info)”
For a deeper understanding of the population analysed by Carroll et al. , the WTs with the following characteristics have been outlined (by the use of “if” cycles on the main table of sheet 2):
- at least 3 year old structure (in 2016)
- geared WTs with an induction machine (either SGIG, WRIG or DFIG)
Among these, structures between 3 and 5 years old and above 5 years old are distinguished as done by the reference.
 Z. Zhang, A. Matveev, S. Øvrebø, R. Nilssen, and A. Nysveen, “State of the art in generator technology for offshore wind energy conversion systems,” in 2011 IEEE International Electric Machines & Drives Conference (IEMDC), 2011, pp. 1131–1136.
 J. Carroll, A. McDonald, and D. McMillan, “Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines,” Wind Energy, vol. 19, pp. 1107–1119, 2016.
 J. Carroll, A. McDonald, I. Dinwoodie, D. McMillan, M. Revie, and I. Lazakis, “Availability, operation and maintenance costs of offshore wind turbines with different drive train configuration,” Wind Energy, vol. 20, no. July 2016, pp. 361–378, 2017.
 J. M. Pinar Pérez, F. P. García Márquez, A. Tobias, and M. Papaelias, “Wind turbine reliability analysis,” Renew. Sustain. Energy Rev., vol. 23, pp. 463–472, 2013.
 WindEurope, “Offshore wind in Europe: Key trends and statistics 2017,” 2018.
The links below were used to extract the majority of the information about the wind farms and their wind turbines, respectively.
Moreover, for these latter, a .zip folder with additional open access information (collected from various sources) is uploaded.