Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines
Accurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. However, traditional numerical modeling a...
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2198 |
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| author | Ruifeng Chen Ke Zhang Min Luo Ye An Lixiang Guo |
| author_facet | Ruifeng Chen Ke Zhang Min Luo Ye An Lixiang Guo |
| author_sort | Ruifeng Chen |
| collection | DOAJ |
| description | Accurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. However, traditional numerical modeling approaches like the finite element method are time-consuming, making them inefficient for generating the extensive datasets required. This paper presents an efficient deep learning-based approach, referred to as the CNN-GRU model, considering multiple external environments. This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. The proposed model is applied to two distinct types of FOWTs under three sea states, and the results demonstrate its satisfactory accuracy, with an average correlation coefficient (CC) of 0.9962 and an average coefficient of determination (R²) of 0.9864. The high accuracy across all cases proves the model’s robustness and reliability. Furthermore, the model’s optimal configurations, including memory lengths, sample sizes, and optimizer, are identified through parametric studies. Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. In addition, a comparative analysis with two other ensemble models, namely random forest and gradient boosting, is conducted. The proposed approach achieves superior accuracy, with computational time approximately half that of the other two models, thereby highlighting its efficiency and effectiveness. The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies. |
| format | Article |
| id | doaj-art-ac5ebf22b0ea478a986dbfbf5daa8491 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ac5ebf22b0ea478a986dbfbf5daa84912025-08-20T02:00:28ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212219810.3390/jmse12122198Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind TurbinesRuifeng Chen0Ke Zhang1Min Luo2Ye An3Lixiang Guo4Ocean College, Zhejiang University, Zhoushan 316021, ChinaZhejiang Zhongnan Green Construction Technology Group Co., Ltd., Hangzhou 310051, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaZhejiang Zhongnan Green Construction Technology Group Co., Ltd., Hangzhou 310051, ChinaAccurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. However, traditional numerical modeling approaches like the finite element method are time-consuming, making them inefficient for generating the extensive datasets required. This paper presents an efficient deep learning-based approach, referred to as the CNN-GRU model, considering multiple external environments. This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. The proposed model is applied to two distinct types of FOWTs under three sea states, and the results demonstrate its satisfactory accuracy, with an average correlation coefficient (CC) of 0.9962 and an average coefficient of determination (R²) of 0.9864. The high accuracy across all cases proves the model’s robustness and reliability. Furthermore, the model’s optimal configurations, including memory lengths, sample sizes, and optimizer, are identified through parametric studies. Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. In addition, a comparative analysis with two other ensemble models, namely random forest and gradient boosting, is conducted. The proposed approach achieves superior accuracy, with computational time approximately half that of the other two models, thereby highlighting its efficiency and effectiveness. The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies.https://www.mdpi.com/2077-1312/12/12/2198response predictionfloating offshore wind turbinedeep learningCNN-GRUSHAP interpretation |
| spellingShingle | Ruifeng Chen Ke Zhang Min Luo Ye An Lixiang Guo Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines Journal of Marine Science and Engineering response prediction floating offshore wind turbine deep learning CNN-GRU SHAP interpretation |
| title | Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines |
| title_full | Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines |
| title_fullStr | Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines |
| title_full_unstemmed | Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines |
| title_short | Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines |
| title_sort | deep learning based prediction of pitch response for floating offshore wind turbines |
| topic | response prediction floating offshore wind turbine deep learning CNN-GRU SHAP interpretation |
| url | https://www.mdpi.com/2077-1312/12/12/2198 |
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