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

Rezultaty

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.

Publikacje

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

Autorzy: Sofia Koukoura, Matti Niclas Scheu, Athanasios Kolios
Opublikowane w: Reliability Engineering & System Safety, Numer 208, 2021, Strona(/y) 107404, ISSN 0951-8320
Wydawca: Elsevier BV
DOI: 10.1016/j.ress.2020.107404

Data-Driven Model Updating of an Offshore Wind Jacket Substructure

Autorzy: Dawid Jakub Augustyn; Ursula Smolka; Ulf T. Tygesen; Martin Dalgaard Ulriksen; John Dalsgaard Sørensen
Opublikowane w: Applied Ocean Research, 2020, ISSN 0141-1187
Wydawca: 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

Autorzy: Ravi Pandit; Athanasios Kolios
Opublikowane w: Applied Sciences, Numer 10/23, 2020, ISSN 2076-3417
Wydawca: 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

Autorzy: Matti Niclas Scheu, Lorena Tremps, Ursula Smolka, Athanasios Kolios, Feargal Brennan
Opublikowane w: Ocean Engineering, Numer 176, 2019, Strona(/y) 118-133, ISSN 0029-8018
Wydawca: Pergamon Press Ltd.
DOI: 10.1016/j.oceaneng.2019.02.048

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

Autorzy: Ravi Kumar Pandit; Athanasios Kolios; David Infield
Opublikowane w: IET Renewable Power Generation, Numer 13/14, 2020, Strona(/y) 2386-2394, ISSN 1752-1416
Wydawca: Institution of Engineering and Technology
DOI: 10.5281/zenodo.7427083

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

Autorzy: D. Cevasco; J. Tautz-Weinert; U. Smolka; A. Kolios
Opublikowane w: Journal of Physics: Conference Series, Numer 1669, 2020, Strona(/y) 012021, ISSN 1742-6588
Wydawca: 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

Autorzy: D. Cevasco; J. Tautz-Weinert; A. J. Kolios; U. Smolka
Opublikowane w: Journal of Physics: Conference Series, Numer 1618, 2020, Strona(/y) 022063, ISSN 1742-6588
Wydawca: 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

Autorzy: D. Cevasco; J. Tautz-Weinert; M. Richmond; A. Sobey; A. J. Kolios
Opublikowane w: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2022, ISSN 2332-9017
Wydawca: 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

Autorzy: Assaf, Roy; Giurgiu, Ioana; Bagehorn, Frank; Schumann, Anika
Opublikowane w: 2019 IEEE International Conference on Data Mining (ICDM), 2020
Wydawca: 2019 IEEE International Conference on Data Mining (ICDM)
DOI: 10.1109/icdm.2019.00106

Risk-based Maintenance Strategies for Offshore Wind Energy Assets

Autorzy: Kolios, Athanasios J.; Smolka, Ursula
Opublikowane w: RAMS 2020 Conference, Numer 3, 2020
Wydawca: RAMS 2020 Conference
DOI: 10.5281/zenodo.3861008

Explainable Deep Neural Networks for Multivariate Time Series Predictions

Autorzy: Roy Assaf, Anika Schumann
Opublikowane w: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019, Strona(/y) 6488-6490, ISBN 978-0-9992411-4-1
Wydawca: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2019/932

Rezultaty

Publikacje

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