Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network
The motion prediction of large floating wind turbine platforms is the key technology to realize the control of active ballast systems and intelligent operation and maintenance monitoring. However, the complex environment of floating wind turbines makes ultra-short-term predictions that only rely on...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2023-10-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
| Subjects: | |
| Online Access: | https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-S1-37.shtml |
| _version_ | 1827763143608107008 |
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| author | WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun |
| author_facet | WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun |
| author_sort | WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun |
| collection | DOAJ |
| description | The motion prediction of large floating wind turbine platforms is the key technology to realize the control of active ballast systems and intelligent operation and maintenance monitoring. However, the complex environment of floating wind turbines makes ultra-short-term predictions that only rely on physical models and numerical simulation methods very challenging. Therefore, this paper proposes an innovative ultra-short-term prediction method for floating wind turbine platform motion based on the long-short-term memory (LSTM) neural network. Measured data have been used to verify the feasibility and uncertainty of this method in terms of surge motion. The results show that the ultra-short-term prediction method proposed in this paper can obtain a better accuracy. For example, the maximum mean square error of surge motion prediction in the 60 s under working condition is only about 1%. The ultra-short-term motion prediction of large floating wind turbines proposed in this paper provides solid technical support for future intelligent operation and maintenance of floating wind farms. |
| first_indexed | 2024-03-11T10:46:56Z |
| format | Article |
| id | doaj.art-64020145c809446fb30d479d5e6bcd8a |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-03-11T10:46:56Z |
| publishDate | 2023-10-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-64020145c809446fb30d479d5e6bcd8a2023-11-14T00:58:09ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672023-10-0157S1374510.16183/j.cnki.jsjtu.2023.S1.27Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM NetworkWEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun01. Shanghai Investigation, Design and Research Institute Co., Ltd., Shanghai 200335, China;2. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3. Sanya Yazhou Bay Institute of Deepsea Science and Technology, Shanghai Jiao Tong University, Sanya 570025, Hainan, ChinaThe motion prediction of large floating wind turbine platforms is the key technology to realize the control of active ballast systems and intelligent operation and maintenance monitoring. However, the complex environment of floating wind turbines makes ultra-short-term predictions that only rely on physical models and numerical simulation methods very challenging. Therefore, this paper proposes an innovative ultra-short-term prediction method for floating wind turbine platform motion based on the long-short-term memory (LSTM) neural network. Measured data have been used to verify the feasibility and uncertainty of this method in terms of surge motion. The results show that the ultra-short-term prediction method proposed in this paper can obtain a better accuracy. For example, the maximum mean square error of surge motion prediction in the 60 s under working condition is only about 1%. The ultra-short-term motion prediction of large floating wind turbines proposed in this paper provides solid technical support for future intelligent operation and maintenance of floating wind farms.https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-S1-37.shtmllarge floating wind turbinesultra-short-term predictionlong-short-term memory (lstm) networkuncertainty |
| spellingShingle | WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network Shanghai Jiaotong Daxue xuebao large floating wind turbines ultra-short-term prediction long-short-term memory (lstm) network uncertainty |
| title | Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network |
| title_full | Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network |
| title_fullStr | Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network |
| title_full_unstemmed | Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network |
| title_short | Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network |
| title_sort | ultra short term platform motion prediction method of large floating wind turbines based on lstm network |
| topic | large floating wind turbines ultra-short-term prediction long-short-term memory (lstm) network uncertainty |
| url | https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-S1-37.shtml |
| work_keys_str_mv | AT weihuichenpengzhangruihanchengzhengshun ultrashorttermplatformmotionpredictionmethodoflargefloatingwindturbinesbasedonlstmnetwork |