A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wind turbine load estimation using machine learning and transfer learning
Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model.
Wind turbine load estimation using machine learning and transfer learning
Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model.
Wind turbine load estimation using machine learning and transfer learning
Xu, Guanqun (author) / Yu, Wei (author) / Kim, Taeseong (author)
2022-01-01
Xu , G , Yu , W & Kim , T 2022 , Wind turbine load estimation using machine learning and transfer learning . in Turbine Technology; Artificial Intelligence, Control and Monitoring . , 032108 , IOP Publishing , Journal of Physics: Conference Series , no. 3 , vol. 2265 , The Science of Making Torque from Wind 2022 , Delft , Netherlands , 01/06/2022 . https://doi.org/10.1088/1742-6596/2265/3/032108
Article (Journal)
Electronic Resource
English
DDC:
690
Using machine learning to predict wind turbine power output
| IOP Institute of Physics | 2013
GPU-Accelerated and Machine-Learning-Based Wind Turbine Damper Optimization
| Springer Verlag | 2022
Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading
| British Library Online Contents | 2014