Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions
The development of floating offshore wind turbines (FOWTs) is gradually moving into deeper offshore areas with more harsh environmental loads, and the corresponding structure response should be paid attention to. Safety assessments need to be conducted based on the evaluation of the long-term extrem...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/9/1807 |
| _version_ | 1827725685417836544 |
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| author | Kelin Wang Oleg Gaidai Fang Wang Xiaosen Xu Tao Zhang Hang Deng |
| author_facet | Kelin Wang Oleg Gaidai Fang Wang Xiaosen Xu Tao Zhang Hang Deng |
| author_sort | Kelin Wang |
| collection | DOAJ |
| description | The development of floating offshore wind turbines (FOWTs) is gradually moving into deeper offshore areas with more harsh environmental loads, and the corresponding structure response should be paid attention to. Safety assessments need to be conducted based on the evaluation of the long-term extreme response under operating conditions. However, the full long-term analysis method (FLTA) recommended by the design code for evaluating extreme response statistics requires significant computational costs. In the present study, a power response prediction method for FOWT based on an artificial neural network algorithm is proposed. FOWT size, structure, and training algorithms from various artificial neural network models to determine optimal network parameters are investigated. A publicly available, high-quality operational dataset is used and processed by the Inverse First Order Reliability Method (IFORM), which significantly reduces simulation time by selecting operating conditions and directly yielding extreme response statistics. Then sensitivity analysis is done regarding the number of neurons and validation check values. Finally, the alternative dataset is used to validate the model. Results show that the proposed neural network model is able to accurately predict the extreme response statistics of FOWT under realistic in situ operating conditions. A proper balance was achieved between prediction accuracy, computational costs, and the robustness of the model. |
| first_indexed | 2024-03-10T22:34:59Z |
| format | Article |
| id | doaj.art-3d3a15ecd6ff46629a783d5675089538 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T22:34:59Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-3d3a15ecd6ff46629a783d56750895382023-11-19T11:27:38ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-09-01119180710.3390/jmse11091807Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating ConditionsKelin Wang0Oleg Gaidai1Fang Wang2Xiaosen Xu3Tao Zhang4Hang Deng5College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaMarine Equipment and Technology Institute, Jiangsu University of Science and Technology, Zhenjiang 212000, ChinaChina Ship Scientific Research Center, Wuxi 214082, ChinaBeijing Zhongke Lianyuan Technology Co., Ltd., Beijing 100000, ChinaThe development of floating offshore wind turbines (FOWTs) is gradually moving into deeper offshore areas with more harsh environmental loads, and the corresponding structure response should be paid attention to. Safety assessments need to be conducted based on the evaluation of the long-term extreme response under operating conditions. However, the full long-term analysis method (FLTA) recommended by the design code for evaluating extreme response statistics requires significant computational costs. In the present study, a power response prediction method for FOWT based on an artificial neural network algorithm is proposed. FOWT size, structure, and training algorithms from various artificial neural network models to determine optimal network parameters are investigated. A publicly available, high-quality operational dataset is used and processed by the Inverse First Order Reliability Method (IFORM), which significantly reduces simulation time by selecting operating conditions and directly yielding extreme response statistics. Then sensitivity analysis is done regarding the number of neurons and validation check values. Finally, the alternative dataset is used to validate the model. Results show that the proposed neural network model is able to accurately predict the extreme response statistics of FOWT under realistic in situ operating conditions. A proper balance was achieved between prediction accuracy, computational costs, and the robustness of the model.https://www.mdpi.com/2077-1312/11/9/1807artificial neural network (ANN)floating offshore wind turbine (FOWT)machine learningextreme responsesinverse first-order reliability method (IFORM)data-driven model |
| spellingShingle | Kelin Wang Oleg Gaidai Fang Wang Xiaosen Xu Tao Zhang Hang Deng Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions Journal of Marine Science and Engineering artificial neural network (ANN) floating offshore wind turbine (FOWT) machine learning extreme responses inverse first-order reliability method (IFORM) data-driven model |
| title | Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions |
| title_full | Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions |
| title_fullStr | Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions |
| title_full_unstemmed | Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions |
| title_short | Artificial Neural Network-Based Prediction of the Extreme Response of Floating Offshore Wind Turbines under Operating Conditions |
| title_sort | artificial neural network based prediction of the extreme response of floating offshore wind turbines under operating conditions |
| topic | artificial neural network (ANN) floating offshore wind turbine (FOWT) machine learning extreme responses inverse first-order reliability method (IFORM) data-driven model |
| url | https://www.mdpi.com/2077-1312/11/9/1807 |
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