A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling
Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/14/5298 |
| _version_ | 1797439616015400960 |
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| author | Alan Turnbull James Carroll Alasdair McDonald |
| author_facet | Alan Turnbull James Carroll Alasdair McDonald |
| author_sort | Alan Turnbull |
| collection | DOAJ |
| description | Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria. |
| first_indexed | 2024-03-09T11:56:39Z |
| format | Article |
| id | doaj.art-7a51022e192c483baf10e886c51f9ce9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T11:56:39Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7a51022e192c483baf10e886c51f9ce92023-11-30T23:09:03ZengMDPI AGEnergies1996-10732022-07-011514529810.3390/en15145298A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour ModellingAlan Turnbull0James Carroll1Alasdair McDonald2Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKInstitute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKInstitute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH9 3DW, UKData-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria.https://www.mdpi.com/1996-1073/15/14/5298wind turbineSCADAmachine learningtemperaturemodellingthreshold |
| spellingShingle | Alan Turnbull James Carroll Alasdair McDonald A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling Energies wind turbine SCADA machine learning temperature modelling threshold |
| title | A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling |
| title_full | A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling |
| title_fullStr | A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling |
| title_full_unstemmed | A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling |
| title_short | A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling |
| title_sort | comparative analysis on the variability of temperature thresholds through time for wind turbine generators using normal behaviour modelling |
| topic | wind turbine SCADA machine learning temperature modelling threshold |
| url | https://www.mdpi.com/1996-1073/15/14/5298 |
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