Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter
To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a...
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MDPI AG
2024-09-01
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| author | Dongmyoung Kim Taesu Jeon Insu Paek Wirachai Roynarin |
| author_facet | Dongmyoung Kim Taesu Jeon Insu Paek Wirachai Roynarin |
| author_sort | Dongmyoung Kim |
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| description | To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power. |
| format | Article |
| id | doaj-art-021b5fa06c0b40cb8e6205bf85c41052 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-021b5fa06c0b40cb8e6205bf85c410522025-08-20T01:47:44ZengMDPI AGApplied Sciences2076-34172024-09-011419876410.3390/app14198764Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman FilterDongmyoung Kim0Taesu Jeon1Insu Paek2Wirachai Roynarin3Department of Integrated Energy and Infra System, Kangwon National University, Chuncheon-si 24341, Gangwon, Republic of KoreaDepartment of Integrated Energy and Infra System, Kangwon National University, Chuncheon-si 24341, Gangwon, Republic of KoreaDepartment of Integrated Energy and Infra System, Kangwon National University, Chuncheon-si 24341, Gangwon, Republic of KoreaDepartment of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, ThailandTo compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.https://www.mdpi.com/2076-3417/14/19/8764wind turbinewind speed estimationfeed-forward controlavailable power |
| spellingShingle | Dongmyoung Kim Taesu Jeon Insu Paek Wirachai Roynarin Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter Applied Sciences wind turbine wind speed estimation feed-forward control available power |
| title | Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter |
| title_full | Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter |
| title_fullStr | Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter |
| title_full_unstemmed | Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter |
| title_short | Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter |
| title_sort | comparison of the wind speed estimation algorithms of wind turbines using a drive train model and extended kalman filter |
| topic | wind turbine wind speed estimation feed-forward control available power |
| url | https://www.mdpi.com/2076-3417/14/19/8764 |
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