Optimal Temperature-Based Condition Monitoring System for Wind Turbines
With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/6/4/50 |
| _version_ | 1827696702119739392 |
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| author | Payam Teimourzadeh Baboli Davood Babazadeh Amin Raeiszadeh Susanne Horodyvskyy Isabel Koprek |
| author_facet | Payam Teimourzadeh Baboli Davood Babazadeh Amin Raeiszadeh Susanne Horodyvskyy Isabel Koprek |
| author_sort | Payam Teimourzadeh Baboli |
| collection | DOAJ |
| description | With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures. |
| first_indexed | 2024-03-10T12:53:10Z |
| format | Article |
| id | doaj.art-566f8b1bab9149318e673cb79b34d768 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T12:53:10Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-566f8b1bab9149318e673cb79b34d7682023-11-21T12:05:24ZengMDPI AGInfrastructures2412-38112021-03-01645010.3390/infrastructures6040050Optimal Temperature-Based Condition Monitoring System for Wind TurbinesPayam Teimourzadeh Baboli0Davood Babazadeh1Amin Raeiszadeh2Susanne Horodyvskyy3Isabel Koprek4R&D Energy Division, OFFIS—Institute for Information Technology, 26121 Oldenburg, GermanyInstitute of Electrical Power and Energy Technology, Hamburg University of Technology, 21073 Hamburg, GermanyR&D Energy Division, OFFIS—Institute for Information Technology, 26121 Oldenburg, GermanyEWE Offshore, Service & Solutions GmbH, 26123 Oldenburg, GermanyEWE Offshore, Service & Solutions GmbH, 26123 Oldenburg, GermanyWith the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.https://www.mdpi.com/2412-3811/6/4/50artificial neural networkcondition-based maintenancehealth monitoringwind turbine |
| spellingShingle | Payam Teimourzadeh Baboli Davood Babazadeh Amin Raeiszadeh Susanne Horodyvskyy Isabel Koprek Optimal Temperature-Based Condition Monitoring System for Wind Turbines Infrastructures artificial neural network condition-based maintenance health monitoring wind turbine |
| title | Optimal Temperature-Based Condition Monitoring System for Wind Turbines |
| title_full | Optimal Temperature-Based Condition Monitoring System for Wind Turbines |
| title_fullStr | Optimal Temperature-Based Condition Monitoring System for Wind Turbines |
| title_full_unstemmed | Optimal Temperature-Based Condition Monitoring System for Wind Turbines |
| title_short | Optimal Temperature-Based Condition Monitoring System for Wind Turbines |
| title_sort | optimal temperature based condition monitoring system for wind turbines |
| topic | artificial neural network condition-based maintenance health monitoring wind turbine |
| url | https://www.mdpi.com/2412-3811/6/4/50 |
| work_keys_str_mv | AT payamteimourzadehbaboli optimaltemperaturebasedconditionmonitoringsystemforwindturbines AT davoodbabazadeh optimaltemperaturebasedconditionmonitoringsystemforwindturbines AT aminraeiszadeh optimaltemperaturebasedconditionmonitoringsystemforwindturbines AT susannehorodyvskyy optimaltemperaturebasedconditionmonitoringsystemforwindturbines AT isabelkoprek optimaltemperaturebasedconditionmonitoringsystemforwindturbines |