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AC-DC coordinated control system is an important technology that can deal with the power exchange and transfer between different power systems. The system controls the operating parameters of AC and DC transmission lines, such as line voltage, phase Angle and power flow, to maintain stable grid operation. The stability control strategy is a very important part in the AC-DC coordinated control system of power grid. It means to adjust the operating state of the power grid by controlling the operating variables of the system to ensure that the power grid can maintain a safe and reliable operating state under both transient and steady state conditions. In this paper, the deep learning theory is applied to the electromagnetic field analysis of the power grid, and the deep learning model is trained and built with the electromagnetic field distribution data corresponding to different power grid structures, so as to predict the electromagnetic field distribution instead of the traditional finite element calculation. Based on the deep learning theory, the convolutional neural network, The in-depth study of the computing process and network structure of the short-duration memory network and its variants determined the hyperparameters needed to build the deep learning model, then established the deep learning model and trained the model through samples, compared the models through evaluation indicators, selected the optimal model, and introduced three different test functions to test the performance of the model. Finally, the optimal model is used to predict the electromagnetic field distribution.
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