Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations
The ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intensification of the load situation within the WT gear...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3659 |
| _version_ | 1797495826063294464 |
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| author | Baher Azzam Ralf Schelenz Georg Jacobs |
| author_facet | Baher Azzam Ralf Schelenz Georg Jacobs |
| author_sort | Baher Azzam |
| collection | DOAJ |
| description | The ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intensification of the load situation within the WT gearbox motivates the need to monitor WT transmission input loads. However, due to the high costs of direct measurement solutions, more economical solutions, such as virtual sensing of transmission input loads using stationary sensors mounted on the gearbox housing or other drivetrain locations, are of interest. As the number, type, and location of sensors needed for a virtual sensing solutions can vary considerably in cost, in this investigation, we aimed to identify optimal sensor locations for virtually sensing WT 6-degree of freedom (6-DOF) transmission input loads. Random forest (RF) models were designed and applied to a dataset containing simulated operational data of a Vestas V52 WT multibody simulation model undergoing simulated wind fields. The dataset contained the 6-DOF transmission input loads and signals from potential sensor locations covering deformations, misalignments, and rotational speeds at various drivetrain locations. The RF models were used to identify the sensor locations with the highest impact on accuracy of virtual load sensing following a known statistical test in order to prioritize and reduce the number of needed input signals. The performance of the models was assessed before and after reducing the number of input signals required. By allowing for a screening of sensors prior to real-world tests, the results demonstrate the high promise of the proposed method for optimizing the cost of future virtual WT transmission load sensors. |
| first_indexed | 2024-03-10T01:55:07Z |
| format | Article |
| id | doaj.art-93811c36437b429991b8dfeee6a5a47d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:07Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-93811c36437b429991b8dfeee6a5a47d2023-11-23T12:58:59ZengMDPI AGSensors1424-82202022-05-012210365910.3390/s22103659Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain SimulationsBaher Azzam0Ralf Schelenz1Georg Jacobs2Center for Wind Power Drives, RWTH Aachen University, 52074 Aachen, GermanyCenter for Wind Power Drives, RWTH Aachen University, 52074 Aachen, GermanyCenter for Wind Power Drives, RWTH Aachen University, 52074 Aachen, GermanyThe ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intensification of the load situation within the WT gearbox motivates the need to monitor WT transmission input loads. However, due to the high costs of direct measurement solutions, more economical solutions, such as virtual sensing of transmission input loads using stationary sensors mounted on the gearbox housing or other drivetrain locations, are of interest. As the number, type, and location of sensors needed for a virtual sensing solutions can vary considerably in cost, in this investigation, we aimed to identify optimal sensor locations for virtually sensing WT 6-degree of freedom (6-DOF) transmission input loads. Random forest (RF) models were designed and applied to a dataset containing simulated operational data of a Vestas V52 WT multibody simulation model undergoing simulated wind fields. The dataset contained the 6-DOF transmission input loads and signals from potential sensor locations covering deformations, misalignments, and rotational speeds at various drivetrain locations. The RF models were used to identify the sensor locations with the highest impact on accuracy of virtual load sensing following a known statistical test in order to prioritize and reduce the number of needed input signals. The performance of the models was assessed before and after reducing the number of input signals required. By allowing for a screening of sensors prior to real-world tests, the results demonstrate the high promise of the proposed method for optimizing the cost of future virtual WT transmission load sensors.https://www.mdpi.com/1424-8220/22/10/3659machine learningartificial intelligencemultivariate data analysisfeature importancevirtual sensingdrivetrain simulation |
| spellingShingle | Baher Azzam Ralf Schelenz Georg Jacobs Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations Sensors machine learning artificial intelligence multivariate data analysis feature importance virtual sensing drivetrain simulation |
| title | Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations |
| title_full | Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations |
| title_fullStr | Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations |
| title_full_unstemmed | Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations |
| title_short | Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations |
| title_sort | sensor screening methodology for virtually sensing transmission input loads of a wind turbine using machine learning techniques and drivetrain simulations |
| topic | machine learning artificial intelligence multivariate data analysis feature importance virtual sensing drivetrain simulation |
| url | https://www.mdpi.com/1424-8220/22/10/3659 |
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