Abstract:

The performance of the transient stability assessment model based on artificial intelligence is highly dependent on the observability of the system, while the factors such as communication delays and PMU faults can easily lead to data missing, which degrades the model's assessment performance. To address this problem, this paper proposes a transient stability assessment model based on graph attention network (GAT). Firstly, a mask matrix representing the system observability is obtained based on the original network topology and PMU configuration scheme, and the mask matrix is used to train the model under the condition of any PMU missing. Secondly, the spatio-temporal information of the input node is extracted through the multi-head attention mechanism of the GAT network, and different weights are used to aggregate the neighborhood characteristics of the target node to make full use of observable data. Finally, the focus loss function is used to enhance the model's learning ability for unstable samples. The simulation results show that the proposed method can maximize the use of observable data with high precision and strong robustness, and is not limited by the network topology and easy to migrate.

Key words: transient stability assessment, data missing, graph attention network, mask matrix, PMU fault