Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods
In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator...
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/3003424 2023-05-15T14:23:33+02:00 Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods Mehlan, Felix Christian Nejad, Amir R. Gao, Zhen 2021 application/pdf https://hdl.handle.net/11250/3003424 https://doi.org/10.1115/OMAE2021-62181 eng eng ASME ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering Norges forskningsråd: 309205 urn:isbn:978-0-7918-8519-2 https://hdl.handle.net/11250/3003424 https://doi.org/10.1115/OMAE2021-62181 cristin:1954900 © ASME Chapter 2021 ftntnutrondheimi https://doi.org/10.1115/OMAE2021-62181 2022-07-13T22:40:03Z In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator torque measurements from SCADA data. With knowledge of the dynamic states local loads at gearbox bearings are easily determined and fatigue models are be applied to track the accumulation of fatigue damage. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered IMS and HSS bearings show moderate to high correlation (R = 0.50–0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15 % from measurements. publishedVersion Book Part Arctic NTNU Open Archive (Norwegian University of Science and Technology) Volume 9: Ocean Renewable Energy |
institution |
Open Polar |
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NTNU Open Archive (Norwegian University of Science and Technology) |
op_collection_id |
ftntnutrondheimi |
language |
English |
description |
In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator torque measurements from SCADA data. With knowledge of the dynamic states local loads at gearbox bearings are easily determined and fatigue models are be applied to track the accumulation of fatigue damage. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered IMS and HSS bearings show moderate to high correlation (R = 0.50–0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15 % from measurements. publishedVersion |
format |
Book Part |
author |
Mehlan, Felix Christian Nejad, Amir R. Gao, Zhen |
spellingShingle |
Mehlan, Felix Christian Nejad, Amir R. Gao, Zhen Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
author_facet |
Mehlan, Felix Christian Nejad, Amir R. Gao, Zhen |
author_sort |
Mehlan, Felix Christian |
title |
Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
title_short |
Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
title_full |
Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
title_fullStr |
Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
title_full_unstemmed |
Estimation of Wind Turbine Gearbox Loads for Online Fatigue Monitoring Using Inverse Methods |
title_sort |
estimation of wind turbine gearbox loads for online fatigue monitoring using inverse methods |
publisher |
ASME |
publishDate |
2021 |
url |
https://hdl.handle.net/11250/3003424 https://doi.org/10.1115/OMAE2021-62181 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering Norges forskningsråd: 309205 urn:isbn:978-0-7918-8519-2 https://hdl.handle.net/11250/3003424 https://doi.org/10.1115/OMAE2021-62181 cristin:1954900 |
op_rights |
© ASME |
op_doi |
https://doi.org/10.1115/OMAE2021-62181 |
container_title |
Volume 9: Ocean Renewable Energy |
_version_ |
1766296077269467136 |