A method of multivariate short-term voltage stability assessment based on heterogeneous graph attention deep network

Abstract

Compared with time-domain simulation (TDS), data-driven models show great advantage in time consumption of the power system security analysis. This paper proposes a novel graph neural network (GNN) based model for the short-term voltage stability (STVS) assessment. Based on mainly the steady-state information as the inputs, the model can provide multivariate stability indices for each bus and adapt to minor topological changes. Both the fast voltage collapses (FVC) and fault-induced delayed voltage recovery (FIDVR) events can be identified. A modified multi-task learning (MTL) process improves the multi-index evaluation performance of the model. As the kernel of the model, a heterogeneous graph attention deep network (HGAT) with various types of nodes works to aggregate the local information and generate new features. Then several micro-detectors scan over the network to provide the STVS indices of each bus with only the feature vector of one node as input for the prediction. Comparisons on the test system show that the proposed method can provide fine-grained STVS evaluation and show good robustness in the scenarios of different dynamic load distribution and network topology changes.