Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind

Leistungen

Portability of failure mode detection/prognosis orientations

This report will collect information related to the portability of the failure modes for diagnosisprognosis orientations

Report on SoA monitoring technology and specification of the support structure monitoring problem for offshore WF

This report will gather the main outputs of the State of the Art analysis carried out with the main aim of identifying the key structure monitoring problems for offshore Wind Farms.

Architecture and Data Framework

This document will gather the main results generated within the development of the data architecture. It is related to the task 5.1.

Report reviewing existing cost and O&M support models and developing an innovative cost model, also considering statistical modelling of key variables

This deliverable will collect information related to the main existing tools/strategies implemented in areas such as cost models and O&M decision support tools.

Report on innovations

This report will gather the main innovations developed throughout the life of the ROMEO project

(5) Report on dissemination and communication activities

This report will monitor the impact of dissemination and communication activities developed in the project

Final report on best practice guidelines for future wind farm structural condition monitoring using low-cost monitoring

This report will consist of a guideline of best practices for the future penetration of lowcost monitoring approaches within WF structural conditioning monitoring

Use-case demonstration into O&M platform

This report is related to the task 6.5 and will collect information produced in the demonstration phase of the O&M platform.

Plan for the Dissemination of Results

This deliverable will establish the basis for the development of common dissemination & exploitation plan in the project.

Report on Life Cycle Assessment of O&M activities offshore with a detailed inventory

This document will gather the results produced in the LCA of O&M activities in offshore. This deliverable is related to the task 8.1.

Failure mode diagnosis/prognosis orientations

This report will collect information related to the failure modes for diagnosis/prognosis orientations.

Integrated tool for impact assessment considering cost and LCA with associated documentation

This deliverable will consist of an integrated cost model tool capable to quantify the benefits of applying effective O&M practices to optimise the CAPEX to OPEX ratio.

Website

This deliverable will be the website of the project.

Veröffentlichungen

Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability

Autoren: Sofia Koukoura, Matti Niclas Scheu, Athanasios Kolios
Veröffentlicht in: Reliability Engineering & System Safety, Ausgabe 208, 2021, Seite(n) 107404, ISSN 0951-8320
Herausgeber: Elsevier BV
DOI: 10.1016/j.ress.2020.107404

Data-Driven Model Updating of an Offshore Wind Jacket Substructure

Autoren: Dawid Jakub Augustyn; Ursula Smolka; Ulf T. Tygesen; Martin Dalgaard Ulriksen; John Dalsgaard Sørensen
Veröffentlicht in: Applied Ocean Research, 2020, ISSN 0141-1187
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.apor.2020.102366

SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

Autoren: Ravi Pandit; Athanasios Kolios
Veröffentlicht in: Applied Sciences, Ausgabe 10/23, 2020, ISSN 2076-3417
Herausgeber: MDPI
DOI: 10.5281/zenodo.7426396

A systematic Failure Mode Effects and Criticality Analysis for offshore wind turbine systems towards integrated condition based maintenance strategies

Autoren: Matti Niclas Scheu, Lorena Tremps, Ursula Smolka, Athanasios Kolios, Feargal Brennan
Veröffentlicht in: Ocean Engineering, Ausgabe 176, 2019, Seite(n) 118-133, ISSN 0029-8018
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2019.02.048

Data-driven weather forecasting modelsperformance comparison for improvingoffshore wind turbine availability andmaintenance

Autoren: Ravi Kumar Pandit; Athanasios Kolios; David Infield
Veröffentlicht in: IET Renewable Power Generation, Ausgabe 13/14, 2020, Seite(n) 2386-2394, ISSN 1752-1416
Herausgeber: Institution of Engineering and Technology
DOI: 10.5281/zenodo.7427083

Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

Autoren: D. Cevasco; J. Tautz-Weinert; U. Smolka; A. Kolios
Veröffentlicht in: Journal of Physics: Conference Series, Ausgabe 1669, 2020, Seite(n) 012021, ISSN 1742-6588
Herausgeber: Institute of Physics
DOI: 10.5281/zenodo.7426474

Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Autoren: D. Cevasco; J. Tautz-Weinert; A. J. Kolios; U. Smolka
Veröffentlicht in: Journal of Physics: Conference Series, Ausgabe 1618, 2020, Seite(n) 022063, ISSN 1742-6588
Herausgeber: Institute of Physics
DOI: 10.5281/zenodo.7426496

A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

Autoren: D. Cevasco; J. Tautz-Weinert; M. Richmond; A. Sobey; A. J. Kolios
Veröffentlicht in: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2022, ISSN 2332-9017
Herausgeber: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
DOI: 10.1115/1.4053659

MTEX-CNN: Multivariate Time Series EXplanations for Predictions with Convolutional Neural Networks

Autoren: Assaf, Roy; Giurgiu, Ioana; Bagehorn, Frank; Schumann, Anika
Veröffentlicht in: 2019 IEEE International Conference on Data Mining (ICDM), 2020
Herausgeber: 2019 IEEE International Conference on Data Mining (ICDM)
DOI: 10.1109/icdm.2019.00106

Risk-based Maintenance Strategies for Offshore Wind Energy Assets

Autoren: Kolios, Athanasios J.; Smolka, Ursula
Veröffentlicht in: RAMS 2020 Conference, Ausgabe 3, 2020
Herausgeber: RAMS 2020 Conference
DOI: 10.5281/zenodo.3861008

Explainable Deep Neural Networks for Multivariate Time Series Predictions

Autoren: Roy Assaf, Anika Schumann
Veröffentlicht in: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019, Seite(n) 6488-6490, ISBN 978-0-9992411-4-1
Herausgeber: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2019/932

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Veröffentlichungen

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