Gintautas, Tomas

English

The costs of operation &amp; maintenance (O&amp;M), assembly, transport and installation of offshore wind turbines (OWT) contribute significantly to the total cost of offshore wind farm. The O&amp;M access and installation operations are mostly carried out by specific ships, that have to be hired for the operational phase and for duration of the installation process, respectively. The duration, and therefore the ship hiring costs, is among others driven by waiting time for weather windows for weathersensitive operations. Today the state of the art decision making criteria for weather-sensitive operations are the significant wave height and the average wind velocity at reference height, however the actual limitations are physical and related to the response of equipment that is used e.g. crane wire tension, air gap between rotor assembly and nacelle, etc. Transition from weather windows to physical response criteria in decision making would improve the cost predictions and, furthermore, potentially reduce the cost of energy. Computer tools can be applied to simulate motions of floating vessels (barges and cranes) representing the access and installation of offshore wind turbines. Since weather predictions have a certain degree of uncertainty the response of the installation equipment is expected to be uncertain also. Assessment and appropriate incorporation of response uncertainties into the decision making processes is essential in order to make accurate decisions

    Aalborg UniversitetDecision Support for Offshore Wind Turbine InstallationStatistical estimation of Probabilities of Failed Offshore OperationsGintautas, TomasPublication date:2015Document VersionPublisher's PDF, also known as Version of recordLink to publication from Aalborg UniversityCitation for published version (APA):Gintautas, T. (2015). Decision Support for Offshore Wind Turbine Installation: Statistical estimation ofProbabilities of Failed Offshore Operations. Poster presented at NORCOWE Science Meets Industry 2015,Stavanger, Norway.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: December 25, 2020Conclusions and future work Objectives Decision Support for Offshore Wind Turbine Installation Statistical estimation of Probabilities of Failed Offshore Operations Tomas Gintautas (P)(1)John Dalsgaard Sørensen(1), Sigrid Ringdalen Vatne (2), Yngve  Heggelund (3) - Project manager (1) Aalborg University, (2) MARINTEK, (3) Christian Michelsen Research Abstract  Methods      NORCOWE Science Meets Industry 16th April, 2015 Weather Input to SIMO Dogger Bank E. The research is funded by The Research Council of Norway, project No. 225231/070 - Decision support for offshore wind turbine installation. The authors also acknowledge Christian Michelsen Research, Meteorologisk Institut (Norway), MARINTEK, Uni Research, University of Bergen (UiB) and STATOIL for their valuable inputs and  support. Weather forecast for FINO3 site ECMWF weather forecasts with 3 days lead time: • Multiple weather parameters (wind speed and direction, wind-sea and swell parameters and directions). • 51 forecast ensembles to ensure low statistical uncertainty (51 weather predictions per lead time).           For each forecasted weather situation (emsemble) 16 realisations are simulated with different seeds within SIMO to ensure stability of the results (according to IEC 61400-1).  This sums up to 816 simulations per lead time. Rotor lift-up from barge and bolting to the nacelle.  The costs of operation & maintenance (O&M), assembly, transport and installation of offshore wind turbines (OWT) contribute significantly to the total cost of offshore wind farm. The O&M access and installation operations are mostly carried out by specific ships, that have to be hired for the operational phase and for duration of the installation process, respectively. The duration, and therefore the ship hiring costs, is among others driven by waiting time for weather windows for weather-sensitive operations.  Today the state of the art decision making criteria for weather-sensitive operations are the significant wave height and the average wind velocity at reference height, however the actual limitations are physical and related to the response of equipment that is used e.g. crane wire tension, air gap between rotor assembly and nacelle, etc. Transition from weather windows to physical response criteria in decision making would improve the cost predictions and, furthermore, potentially reduce the cost of energy.  Computer tools can be applied to simulate motions of floating vessels (barges and cranes) representing the access and installation of offshore wind turbines. Since weather predictions have a certain degree of uncertainty the response of the installation equipment is expected to be uncertain also. Assessment and appropriate incorporation of response uncertainties into the decision making processes is essential in order to make accurate decisions.  Weather forecasts  are passed through SIMO and response time series are analysed statistically in order to obtain Probabilities of Failed operations: 1. Peak Over Threshold method is applied to extract extreme values of relevant responses. 2. Weibull or Gumbel distribution is fitted to the extremes using Maximum Likelihood parameter estimation. 3. Steps 1-2 are repeated for 51 forecast ensembles individually (example lead time 36 hours).  ECMWF Ensembles: Forecasted weather conditions Graphical representation of the model SIMO: SImulation of complex Marine Operations Operation phase input (cranes, vessels, WT components). Time series of relevant responses (equipment loads, motions) STATISTICAL MODEL: Estimation of statistical  parameters of  extreme responses Uncertain operation limit parameters (maximum crane loads, allowable motions). Estimation of modelling uncertainties, when model tests are available. Estimates of probability of operation failure Decision making based on combination of  Costs and Probabilities of failed operations • Present a general framework for Risk Based Decision Making for OWT installation process. • Present the current status of the development of the Decision Support Tool. • Discuss limitations of current knowlege on Operation limits.  Acknowledgments   phases LSNiNjjiLSjiLSequipmentwaitingtotal CPCCC1 1,,,,Phase 5.  Lift rotor up close to nacelle Crane loads < 4250 kN Lift wire tension > 0 kN Air gap blade 3 and tower  > 0 m Rotational and absolute rotor acceleration < 1 and 4 rad/s2 Yaw and tilt angle <  5 degrees Rotor sway and surge motion < 1 m SIMO software is used to simulate the installation sequence using systems of barges, cranes, control wires/tugs  and wind turbine components. The installation process is split into different phases (transportation, positioning, lifting of the rotor, etc.). Each phase has multiple failure criteria, example: 1 2 3 Simulation model, output and postprocessing 4 4 After extensive testing it can be concluded that the procedure for estimation of Probability of  Failed Operations produces consistent results and could be used to assist in decision making for Offshore Wind Turbine installation. Although, due to lack of available information about the actual physical operational limits, it has to be noted that the example case only acts as a proof of concept. By combining probabilities with expected costs of failed operations, the procedure can be also used for Risk Based decision making. Future work would include expanding the tool to include Costs of Failure, more clear definition of actual physical operational limits (with related uncertainties), tests with scaled installation models for estimating model uncertainties, implementation of Bayesian statistical framework to update the model with on site measurements of equipment responses and/or weather conditions 4. The Probability of Failure for one limit state in one phase is estimated using the 51 ensembles. Summing up all the limits states in one phase gives Probability of failure within an operation phase.  𝑃𝐹,𝐿𝑖𝑚 𝑆𝑡𝑎𝑡𝑒 = 𝑃𝐹,𝐸𝑛𝑠𝑒𝑚𝑏𝑙𝑒𝑁𝑖=1𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑛𝑠  𝑃𝐹,𝑃ℎ𝑎𝑠𝑒=  𝑃𝐹,𝐿𝑖𝑚 𝑆𝑡𝑎𝑡𝑒,𝑖𝑁𝐿𝑖𝑚 𝑆𝑡𝑎𝑡𝑒𝑠𝑖=1    

Decision Support for Offshore Wind Turbine Installation:Statistical estimation of Probabilities of Failed Offshore Operations

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Decision Support for Offshore Wind Turbine Installation:Statistical estimation of Probabilities of Failed Offshore Operations

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