Static voltage stability margin prediction considering new energy uncertainty based on graph attention networks and long short-term memory networks

The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short-term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT-LSTM is designed to extract highly representative power grid operation features considering the spatial-temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT-LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39-bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability.