Structural Displacement Prediction of Power Transmission Tower using Finite-Element Modelling and Deep Learning

The operation safety of power transmission infrastructure takes an important role in power industrial applications. This paper investigates the problem of structural displacement predication of a power transmission tower for online monitoring and early warning. First, finite-element modelling (FEM) is addressed to establish the digital model of a studied power transmission tower. Additionally, key monitoring positions of the tower are determined by the FEM under vibration analysis. At last, a recurrent neural network is proposed to forecast the displacements of the key monitoring positions ahead of time steps. The experimental results demonstrate the implementation and performance of the proposed method for displacement prediction of a power transmission tower.