Data-driven operations and maintenance for offshore wind farms: tools and methodologies
Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operations and maintenance (O&M) of these assets have to become increasingly efficient, whilst ensuring com...
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Other Authors: | , , , |
Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
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The University of Edinburgh
2020
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Online Access: | https://hdl.handle.net/1842/36710 https://doi.org/10.7488/era/17 |
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ftunivedinburgh:oai:era.ed.ac.uk:1842/36710 |
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Edinburgh Research Archive (ERA - University of Edinburgh) |
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ftunivedinburgh |
language |
English |
topic |
offshore wind turbines offshore wind operations and maintenance cost reduction reliability data analytics maintenance optimization decision-making |
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offshore wind turbines offshore wind operations and maintenance cost reduction reliability data analytics maintenance optimization decision-making Koltsidopoulos Papatzimos, Alexios Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
topic_facet |
offshore wind turbines offshore wind operations and maintenance cost reduction reliability data analytics maintenance optimization decision-making |
description |
Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operations and maintenance (O&M) of these assets have to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous operational data. These data contain rich information about the condition of the assets, which are rarely fully utilized by the operators and service providers. This thesis provides useful methodologies and tools that can help wind farm owners, operators and service providers to reduce their O&M costs by better managing their data, integrating processes and providing data-driven decision making.The developed methodologies and tools are being presented through several case studies, showing the effectiveness of the solutions and their potential cost reduction opportunities. These are split into the following four themes:i. Data management techniques, methodologies and case studies, aiming to improve data collection and data integration strategies for a data informed decision making.ii. Processes and best practices for workflow improvements and automated datacollection and standardization.iii. Data analytics including reliability, diagnostic and prognostic methodologies and case studies.iv. Maintenance planning including enhanced planning strategies, decision support frameworks and optimized maintenance operations.All of the above frameworks, methodologies and case studies are linked together as they provide insights for data-driven decision making, which results in better informed and thus less costly maintenance strategies.The methodologies and case studies presented will assist in creating data-driven O&M processes and allowing the full utilization of the produced offshore wind farm data. |
author2 |
Thies, Philipp Kurt, Rafet Jeffrey, Henry Engineering and Physical Sciences Research Council (EPSRC) |
format |
Doctoral or Postdoctoral Thesis |
author |
Koltsidopoulos Papatzimos, Alexios |
author_facet |
Koltsidopoulos Papatzimos, Alexios |
author_sort |
Koltsidopoulos Papatzimos, Alexios |
title |
Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
title_short |
Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
title_full |
Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
title_fullStr |
Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
title_full_unstemmed |
Data-driven operations and maintenance for offshore wind farms: tools and methodologies |
title_sort |
data-driven operations and maintenance for offshore wind farms: tools and methodologies |
publisher |
The University of Edinburgh |
publishDate |
2020 |
url |
https://hdl.handle.net/1842/36710 https://doi.org/10.7488/era/17 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R.(2017) Towards automated and integrated data collection-standardising workflowprocesses for the offshore wind industry. InProc. of Offshore Wind Energy 2017,6-8 June, London, UK. Koltsidopoulos Papatzimos,A., Dawood, T., Thies, P.R. (2017) An Integrated Data Management Approachfor Offshore Wind Turbine Failure Root Cause Analysis. InProc.of 36thInternational Conference on Ocean, Offshore and Arctic Engineering (OMAE),OMAE2017-62279, 25–30 June, Trondheim, Norway. Koltsidopoulos Papatzimos, A., Dawood, T.,Thies, P.R. (2018). Data Insights from an Offshore Wind Turbine GearboxReplacement.Journal of Physics: Conference Series,1104(1). Koltsidopoulos Papatzimos, A.,Dawood, T., Thies, PR. (2018) Operational Data to Maintenance Optimization:Closing the Loop in Offshore Wind O&M. InProc. of the ASME 2018 1st International Offshore Wind Technical Conference (IOWTC), 4-7 Nov, SanFrancisco, USA Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R. (2017) On Risk-based Inspections for Offshore Wind Farms: A Case Study. InProc. of56th Annual British Conf. of Non-Destructive Testing (NDT 2017), TheBritish Institute of Non-Destructive Testing (BINDT), 4-7 Sept, Telford,UK. Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R. (2018) Costeffective, risk-based inspection planning for offshore wind farms.Insight- Non-Destructive Testing and Condition Monitoring,60(6), 299-305. Koltsidopoulos Papatzimos, A., Thies, P.R.,Lonchampt, J., Joly, A., Dawood, T., (2019) Data Informed Lifetime ReliabilityPrediction for Offshore Wind Farms. In2019 IEEE International Conference onPrognostics and Health Management (ICPHM), San Francisco, CA, 2018. https://hdl.handle.net/1842/36710 http://dx.doi.org/10.7488/era/17 |
op_doi |
https://doi.org/10.7488/era/17 |
_version_ |
1772810723107274752 |
spelling |
ftunivedinburgh:oai:era.ed.ac.uk:1842/36710 2023-07-30T04:00:08+02:00 Data-driven operations and maintenance for offshore wind farms: tools and methodologies Koltsidopoulos Papatzimos, Alexios Thies, Philipp Kurt, Rafet Jeffrey, Henry Engineering and Physical Sciences Research Council (EPSRC) 2020-01-21 application/pdf https://hdl.handle.net/1842/36710 https://doi.org/10.7488/era/17 en eng The University of Edinburgh Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R.(2017) Towards automated and integrated data collection-standardising workflowprocesses for the offshore wind industry. InProc. of Offshore Wind Energy 2017,6-8 June, London, UK. Koltsidopoulos Papatzimos,A., Dawood, T., Thies, P.R. (2017) An Integrated Data Management Approachfor Offshore Wind Turbine Failure Root Cause Analysis. InProc.of 36thInternational Conference on Ocean, Offshore and Arctic Engineering (OMAE),OMAE2017-62279, 25–30 June, Trondheim, Norway. Koltsidopoulos Papatzimos, A., Dawood, T.,Thies, P.R. (2018). Data Insights from an Offshore Wind Turbine GearboxReplacement.Journal of Physics: Conference Series,1104(1). Koltsidopoulos Papatzimos, A.,Dawood, T., Thies, PR. (2018) Operational Data to Maintenance Optimization:Closing the Loop in Offshore Wind O&M. InProc. of the ASME 2018 1st International Offshore Wind Technical Conference (IOWTC), 4-7 Nov, SanFrancisco, USA Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R. (2017) On Risk-based Inspections for Offshore Wind Farms: A Case Study. InProc. of56th Annual British Conf. of Non-Destructive Testing (NDT 2017), TheBritish Institute of Non-Destructive Testing (BINDT), 4-7 Sept, Telford,UK. Koltsidopoulos Papatzimos, A., Dawood, T., Thies, P.R. (2018) Costeffective, risk-based inspection planning for offshore wind farms.Insight- Non-Destructive Testing and Condition Monitoring,60(6), 299-305. Koltsidopoulos Papatzimos, A., Thies, P.R.,Lonchampt, J., Joly, A., Dawood, T., (2019) Data Informed Lifetime ReliabilityPrediction for Offshore Wind Farms. In2019 IEEE International Conference onPrognostics and Health Management (ICPHM), San Francisco, CA, 2018. https://hdl.handle.net/1842/36710 http://dx.doi.org/10.7488/era/17 offshore wind turbines offshore wind operations and maintenance cost reduction reliability data analytics maintenance optimization decision-making Thesis or Dissertation Doctoral PhD Doctor of Philosophy 2020 ftunivedinburgh https://doi.org/10.7488/era/17 2023-07-09T20:29:39Z Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operations and maintenance (O&M) of these assets have to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous operational data. These data contain rich information about the condition of the assets, which are rarely fully utilized by the operators and service providers. This thesis provides useful methodologies and tools that can help wind farm owners, operators and service providers to reduce their O&M costs by better managing their data, integrating processes and providing data-driven decision making.The developed methodologies and tools are being presented through several case studies, showing the effectiveness of the solutions and their potential cost reduction opportunities. These are split into the following four themes:i. Data management techniques, methodologies and case studies, aiming to improve data collection and data integration strategies for a data informed decision making.ii. Processes and best practices for workflow improvements and automated datacollection and standardization.iii. Data analytics including reliability, diagnostic and prognostic methodologies and case studies.iv. Maintenance planning including enhanced planning strategies, decision support frameworks and optimized maintenance operations.All of the above frameworks, methodologies and case studies are linked together as they provide insights for data-driven decision making, which results in better informed and thus less costly maintenance strategies.The methodologies and case studies presented will assist in creating data-driven O&M processes and allowing the full utilization of the produced offshore wind farm data. Doctoral or Postdoctoral Thesis Arctic Edinburgh Research Archive (ERA - University of Edinburgh) |