PREDICTION OF VERTICAL DISPLACEMENT OF ONSHORE WIND TURBINE FOUNDATION BASED ON 3DCNN
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摘要:
为准确预测风机基础的沉降,避免风机基础不均匀沉降过大导致风机结构变形,影响安全及寿命,依托某陆上风机基础加固项目,构建了一种基于三维卷积神经网络(three-dimensional convolutional neural network, 3DCNN)的风机基础竖向位移预测模型,对不同位置测点的竖向位移监测数据进行时空重构,并通过时空矩阵将监测数据导入三维卷积核学习数据间的时空特征。对比当下热门的神经网络模型,可以发现3DCNN模型在沉降预测方面具有更高的预测精度,其泛化性和稳定性也更优越。
Abstract:In order to accurately predict the settlement of the wind turbine foundation and avoid the deformation of the wind turbine structure due to the excessive uneven settlement of the wind turbine foundation, which affects the safety and life span of the wind turbine, a vertical displacement prediction model of the wind turbine foundation based on a three-dimensional convolutional neural network (three-dimensional convolutional neural network, 3DCNN) is constructed, originated from a certain onshore wind turbine foundation reinforcement project. During the construction process of the proposed 3DCNN model, the vertical displacement monitoring data from the measurement points at different locations are spatio-temporally reconstructed firstly. The monitoring data is then imported into a spatio-temporal matrix, where the 3D convolutional kernel learns the spatio-temporal features between the data. Comparing with several prevalent neural network models, the 3DCNN model is found to have higher prediction accuracy in settlement prediction, as well as superior generalization and stability.
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表 1 各模型测试集预测结果对比
Table 1 Comparison of prediction results on test set for various models
MSE MAE LSTM 0.214 0.352 CNN-LSTM 0.197 0.336 3DCNN 0.185 0.323 -
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