Health monitoring of renewable energy systems
The offshore wind energy industry has grown exponentially; globally, there is 12GW of installed capacity of offshore wind, of which over 95% has been installed in the past ten years. Access and maintenance in offshore wind farms can be difficult and considerably more expensive than onshore wind farm...
Published in: | 2018 2nd International Conference on Green Energy and Applications (ICGEA) |
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Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
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The University of Edinburgh
2019
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Online Access: | http://hdl.handle.net/1842/36085 |
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Edinburgh Research Archive (ERA - University of Edinburgh) |
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reliability offshore wind turbines data mining physics based models failure diagnosis failure prognosis risk assessment FMEA wind turbine maintenance wind turbine CBM |
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reliability offshore wind turbines data mining physics based models failure diagnosis failure prognosis risk assessment FMEA wind turbine maintenance wind turbine CBM Sepulveda Gutierrez, Marco Antonio Health monitoring of renewable energy systems |
topic_facet |
reliability offshore wind turbines data mining physics based models failure diagnosis failure prognosis risk assessment FMEA wind turbine maintenance wind turbine CBM |
description |
The offshore wind energy industry has grown exponentially; globally, there is 12GW of installed capacity of offshore wind, of which over 95% has been installed in the past ten years. Access and maintenance in offshore wind farms can be difficult and considerably more expensive than onshore wind farms. Additionally, with low availability levels and greater downtime due to failures, there is a growing interest in the optimisation of operation and maintenance (O&M) activities to maximise profitability. Traditionally, maintenance activities on critical components and subsystems have deployed two maintenance approaches; time-based preventative or corrective. Time-based preventative or scheduled maintenance approaches are based on intervening at fixed intervals, determined in advance for each component. Scheduling is based on failure statistics such as mean time between failures (MTBF), mean time to repair (MTTR) or mean time to failure (MTTF). These come either from publicly available databases or operational measurements. As part of preventive maintenance activities, there are annual services of the turbine to replace and maintain any component or assembly based on manufacturers’ indications. On the other hand, the corrective maintenance approach involves operating equipment until it fails and then restoring it, repairing it, or replacing it. Due to conservative estimates regarding the probability of failure, preventive and corrective maintenance approaches have financial implications associated with them. In the preventive approach, components are frequently replaced before they reach the end of their working life. In contrast, corrective maintenance guarantees that the serviceable life of a component is maximised, but it is subjected to long downtime, which is expensive regarding energy generation loss. Additionally, failure of the component may cause consequential damage to other parts of the wind turbine system, resulting in even greater repair costs, downtime and loss of revenue. A comprehensive literature ... |
author2 |
Shek, Jonathan Oterkus, Erkan Thies, Philipp |
format |
Doctoral or Postdoctoral Thesis |
author |
Sepulveda Gutierrez, Marco Antonio |
author_facet |
Sepulveda Gutierrez, Marco Antonio |
author_sort |
Sepulveda Gutierrez, Marco Antonio |
title |
Health monitoring of renewable energy systems |
title_short |
Health monitoring of renewable energy systems |
title_full |
Health monitoring of renewable energy systems |
title_fullStr |
Health monitoring of renewable energy systems |
title_full_unstemmed |
Health monitoring of renewable energy systems |
title_sort |
health monitoring of renewable energy systems |
publisher |
The University of Edinburgh |
publishDate |
2019 |
url |
http://hdl.handle.net/1842/36085 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Marco Sepulveda, Dr Jonathan Shek, Dr Philipp R. Thies. Risk assessment of an offshore wind turbine and development of a physics of failure based approach to estimate the remaining useful life (RUL) of the power converter. International Conference on Offshore Renewable Energy CORE 2016. Glasgow, UK. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring, Cengiz Yilmaz. Remaining Useful Life Estimation of Gearboxes through Combined Statistical and Physics-based Offshore Wind Turbine Modelling. American Wind Energy Association (AWEA) Conference, Warwick USA, 2016. Marco Sepúlveda, Mark Spring, Peter Davies, Dr Jonathan Shek, Dr Philipp R. Thies, Dr Erkan Oterkus. Risk Management in O&M for Offshore Wind Generation. Offshore Wind Operations & Maintenance Forum BIS Group, London, 2016 Mark Spring, Marco Sepúlveda, Peter Davies, Gerard Gaal. Top 30 Chart for wind turbine failure mechanisms. European Wind Energy Association (EWEA) Conference 2015. Paris. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Physics-based gearbox failure model for multi-MWMW offshore wind turbines. Proceedings of the 36th International Conference on Ocean, Offshore & Arctic Engineering ASME OMAE17, Trondheim, Norway, 2017. Krishnamoorthi Sivalingam, Dr Mark Spring, Peter Davies, Marco Sepulveda. A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fixed and Floating Wind turbine Power Converter with Digital Twin Technology Perspective. IEEE, 2nd International Conference on Green Energy and Applications (ICGEA), Singapore, 2018. DOI:10.1109/ICGEA.2018.8356292 Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Pitch system failure identification using a combination of subject matter expert knowledge of offshore wind turbines and machine learning techniques. Offshore Wind Energy Conference WindEurope, London, 2017. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Offshore wind farm O&M optimisation: using an integral approach for failure diagnosis and prognosis. All-Energy Conference and Exhibition, Glasgow, 2017. http://hdl.handle.net/1842/36085 |
op_doi |
https://doi.org/10.1109/ICGEA.2018.8356292 |
container_title |
2018 2nd International Conference on Green Energy and Applications (ICGEA) |
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ftunivedinburgh:oai:era.ed.ac.uk:1842/36085 2023-07-30T04:00:08+02:00 Health monitoring of renewable energy systems Sepulveda Gutierrez, Marco Antonio Shek, Jonathan Oterkus, Erkan Thies, Philipp 2019-11-28 application/pdf http://hdl.handle.net/1842/36085 en eng The University of Edinburgh Marco Sepulveda, Dr Jonathan Shek, Dr Philipp R. Thies. Risk assessment of an offshore wind turbine and development of a physics of failure based approach to estimate the remaining useful life (RUL) of the power converter. International Conference on Offshore Renewable Energy CORE 2016. Glasgow, UK. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring, Cengiz Yilmaz. Remaining Useful Life Estimation of Gearboxes through Combined Statistical and Physics-based Offshore Wind Turbine Modelling. American Wind Energy Association (AWEA) Conference, Warwick USA, 2016. Marco Sepúlveda, Mark Spring, Peter Davies, Dr Jonathan Shek, Dr Philipp R. Thies, Dr Erkan Oterkus. Risk Management in O&M for Offshore Wind Generation. Offshore Wind Operations & Maintenance Forum BIS Group, London, 2016 Mark Spring, Marco Sepúlveda, Peter Davies, Gerard Gaal. Top 30 Chart for wind turbine failure mechanisms. European Wind Energy Association (EWEA) Conference 2015. Paris. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Physics-based gearbox failure model for multi-MWMW offshore wind turbines. Proceedings of the 36th International Conference on Ocean, Offshore & Arctic Engineering ASME OMAE17, Trondheim, Norway, 2017. Krishnamoorthi Sivalingam, Dr Mark Spring, Peter Davies, Marco Sepulveda. A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fixed and Floating Wind turbine Power Converter with Digital Twin Technology Perspective. IEEE, 2nd International Conference on Green Energy and Applications (ICGEA), Singapore, 2018. DOI:10.1109/ICGEA.2018.8356292 Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Pitch system failure identification using a combination of subject matter expert knowledge of offshore wind turbines and machine learning techniques. Offshore Wind Energy Conference WindEurope, London, 2017. Marco A. Sepulveda, Dr. Jonathan Shek, Dr. Philipp Thies, Dr. Erkan Oterkus, Mr. Peter Davies, Dr. Mark Spring. Offshore wind farm O&M optimisation: using an integral approach for failure diagnosis and prognosis. All-Energy Conference and Exhibition, Glasgow, 2017. http://hdl.handle.net/1842/36085 reliability offshore wind turbines data mining physics based models failure diagnosis failure prognosis risk assessment FMEA wind turbine maintenance wind turbine CBM Thesis or Dissertation Doctoral PhD Doctor of Philosophy 2019 ftunivedinburgh https://doi.org/10.1109/ICGEA.2018.8356292 2023-07-09T20:33:22Z The offshore wind energy industry has grown exponentially; globally, there is 12GW of installed capacity of offshore wind, of which over 95% has been installed in the past ten years. Access and maintenance in offshore wind farms can be difficult and considerably more expensive than onshore wind farms. Additionally, with low availability levels and greater downtime due to failures, there is a growing interest in the optimisation of operation and maintenance (O&M) activities to maximise profitability. Traditionally, maintenance activities on critical components and subsystems have deployed two maintenance approaches; time-based preventative or corrective. Time-based preventative or scheduled maintenance approaches are based on intervening at fixed intervals, determined in advance for each component. Scheduling is based on failure statistics such as mean time between failures (MTBF), mean time to repair (MTTR) or mean time to failure (MTTF). These come either from publicly available databases or operational measurements. As part of preventive maintenance activities, there are annual services of the turbine to replace and maintain any component or assembly based on manufacturers’ indications. On the other hand, the corrective maintenance approach involves operating equipment until it fails and then restoring it, repairing it, or replacing it. Due to conservative estimates regarding the probability of failure, preventive and corrective maintenance approaches have financial implications associated with them. In the preventive approach, components are frequently replaced before they reach the end of their working life. In contrast, corrective maintenance guarantees that the serviceable life of a component is maximised, but it is subjected to long downtime, which is expensive regarding energy generation loss. Additionally, failure of the component may cause consequential damage to other parts of the wind turbine system, resulting in even greater repair costs, downtime and loss of revenue. A comprehensive literature ... Doctoral or Postdoctoral Thesis Arctic Edinburgh Research Archive (ERA - University of Edinburgh) 2018 2nd International Conference on Green Energy and Applications (ICGEA) 197 204 |