Browsing by Author "Cevasco, Debora"

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    Dataset for European Installed Offshore Wind Turbines (until year end 2017)
    (Cranfield University, 2018-06-11 09:18) Cevasco, Debora; Collu, Maurizio
    Introduction and aimThis 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. [1, 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. [2, [3 (in their reliability database), are identified and analysed more in details.Dataset organisationThe 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. [1 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 WFsheet 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. [4 (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 [5, 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. [2, 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.References[1 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.[2 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.[3 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.[4 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.[5 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.https://www.4coffshore.com/windfarms/https://en.wind-turbine-models.com/turbinesMoreover, for these latter, a .zip folder with additional open access information (collected from various sources) is uploaded.
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    Human-free offshore lifting solutions
    (2018-10-10) Leimeister, Mareike; Balaam, T.; Causon, Paul Douglas; Cevasco, Debora; Richmond, M.; Kolios, Athanasios; Brennan, Feargal
    With single elements weighing up to hundreds of tonnes and lifted to heights of 100 meters, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. To increase safety during offshore lifts, this study focuses on solutions for human-free lifting operations. Ideas in the categories of logistics, connections, as well as guidance and control, were discussed and ranked by means of a multi-criteria decision analysis. Based upon 38 survey responses weighting 21 predefined decision criteria, the most promising concepts were selected. Logistically, pre-assembled systems would reduce the number of lifts and thus reduce the risk. A MATLAB-based code has been developed to optimise installation time, lifted weight, and number of lifts. Automated bolting and seafastening solutions have high potential to increase safety during the transport of the wind turbine elements and, additionally, speed up the process. Finally, the wind turbine should be lifted on top of the support structure without having personnel being under the load. A multi-directional mechanical guiding element has been designed and tested successfully in combination with visual guidance by cameras in a small-scale experiment.
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    Industry survey response of criteria weights for lifting technologies in the offshore wind energy environment.
    (Cranfield University, 2018-06-12 13:10) Richmond, Mark; Balaam, Toby; douglas Causon, Paul; Cevasco, Debora; Leimeister, Mareike
    Industry response data to the survey conducted for the journal article titled 'Multi-Criteria Decision Analysis for Benchmarking Human-Free Lifting Solutions in the Offshore Wind Energy Environment'
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    Multi-criteria decision analysis for benchmarking human-free lifting solutions in the offshore wind energy environment
    (MDPI, 2018-05-07) Richmond, Mark; Balaam, Toby; Causon, Paul; Cevasco, Debora; Leimeister, Mareike; Kolios, Athanasios; Brennan, Feargal
    With single components weighing up to hundreds of tonnes and lifted to heights of approximately 100 m, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. Guidelines and standards for health and safety in lifting operations exist; however, having people directly beneath the load is still common practice in offshore wind turbine installations. Concepts for human-free offshore lifting operations in the categories of guidance and control, connections, and assembly are studied in this work. This paper documents the process of applying Multi-Criteria Decision Analysis (MCDA), using experts’ opinions for the importance of defined criteria obtained by conducting an industry survey, to benchmark the suitability of the concepts at two stages. Stage one streamlined possible options and stage two ranked the remaining suite of options after further development. The survey results showed that criteria such as ‘reduction of risk’, ‘handling improvement’ and ‘reliability of operation’ were most important. The most viable options, weighted by industry opinion, to remove personnel from areas of high risk are: Boom Lock and tag lines, a camera system with mechanical guidance, and automated bolt installation/fastening for seafastening. The decision analysis framework developed can be applied to similar problems to inform choices subject to multiple criteria.
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    O&M cost-based FMECA: Identification and ranking of the most critical components for 2-4 MW geared offshore wind turbines
    (IOP, 2018-10-10) Cevasco, Debora; Collu, Maurizio; Lin, Z.
    To date, the focus of the research on offshore wind turbines (WTs) has been mainly on how to minimise their capital cost, but Operation and Maintenance (O&M) can represent up to a third of the lifetime costs of an offshore wind farm. The cost for the assets repair/replacement and for the logistics of the maintenance operations are two of the biggest contributors to O&M expenses. While the first is going to rise with the employment of bigger structures, the latter can significantly increase dependently on the reliability of the components, and thus the necessity to performed unscheduled maintenance operations. Using the reliability data for a population of offshore WTs (representing the configurations most employed offshore), first, the share of the components failures to the O&M cost, together with an estimation of their dependency on some O&M parameters has been derived. Then, by following a cost-based Failure Modes Effects and Criticality Analysis (FMECA), and ranking the components through O&M cost priority number, the most critical components for O&M unplanned operations are identified.
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    On mooring line tension and fatigue prediction for offshore vertical axis wind turbines: A comparison of lumped mass and quasi-static approaches
    (SAGE, 2018-03-20) Cevasco, Debora; Collu, Maurizio; Rizzo, C. M.; Hall, M.
    Despite several potential advantages, relatively few studies and design support tools have been developed for floating vertical axis wind turbines. Due to the substantial aerodynamics differences, the analyses of vertical axis wind turbine on floating structures cannot be easily extended from what have been already done for horizontal axis wind turbines. Therefore, the main aim of the present work is to compare the dynamic response of the floating offshore wind turbine system adopting two different mooring dynamics approaches. Two versions of the in-house aero-hydro-mooring coupled model of dynamics for floating vertical axis wind turbine (FloVAWT) have been used, employing a mooring quasi-static model, which solves the equations using an energetic approach, and a modified version of floating vertical axis wind turbine, which instead couples with the lumped mass mooring line model MoorDyn. The results, in terms of mooring line tension, fatigue and response in frequency have been obtained and analysed, based on a 5 MW Darrieus type rotor supported by the OC4-DeepCwind semisubmersible.