Abstract
With the development of HVDC transmission and changes of load composition and characteristics, the short-term voltage problem seriously threatens the safety and stable operation of power systems. Based on deep learning, a fast short-term voltage stability assessment method for AC/DC receiving-end power grid is proposed in this paper. Considering the influence of multi-infeed DC and dynamic loads, convolutional neural network is adopted in this paper, which use the steady-state power flow and fault features as inputs and the short-term voltage stability of the area around the DC drop point as the output. Simulation results of real multi-infeed AC/DC power grid demonstrate the effectiveness of the proposed method.
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