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Prediction method for galloping features of transmission lines based on FEM and machine learning

机译：基于有限元和机器学习的输电线路舞动特性预测方法

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摘要

Parameter study on galloping of iced transmission lines with different structural, ice and wind parameters is carried out by means of the finite element method (FEM), and a dataset is then created based on the numerical results. The random forests algorithm is used to set up a classification model to predict the galloping mode and vertical vibration mode. Using the span length, initial conductor tension, ice thickness, initial wind attack angle, wind velocity and the galloping mode as well as the vertical vibration mode obtained by the classification model as the input variables, a back-propagation (BP) neural network model is created to predict the galloping features including the frequencies, vibration amplitudes and the maximum conductor tension. The dataset created based on the parameter study is divided into two sub-datasets for training and testing of the two machine learning models respectively. The effects of the input variables on the output variables are quantified with a feature importance analysis for the classification model and a global sensitivity analysis for the feature prediction model respectively. The two models are finally integrated into one to predict galloping features efficiently and quickly. Making use of the proposed method, an early warning system for galloping of transmission lines may be able to be set up in the future.
机译：利用有限元方法（FEM）对不同结构，冰，风参数的输电线路的舞动进行了参数研究，并根据数值结果建立了数据集。随机森林算法用于建立分类模型，以预测舞动模式和垂直振动模式。将通过分类模型获得的跨度长度，初始导体张力，冰厚度，初始风侵角，风速和舞动模式以及垂直振动模式作为输入变量，使用反向传播（BP）神经网络模型被创建来预测舞动特征，包括频率，振动幅度和最大导体张力。基于参数研究创建的数据集分为两个子数据集，分别用于训练和测试两个机器学习模型。输入变量对输出变量的影响分别通过分类模型的特征重要性分析和特征预测模型的全局敏感性分析进行量化。最终将这两个模型集成为一个模型，以高效，快速地预测舞动特征。利用提出的方法，将来可能会建立一个疾驰的传输线预警系统。

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