Online Power System Dynamic Security Assessment: A GNN–FNO Approach Learning From Multisource Spatial–Temporal Data

Data-driven online dynamic security assessment offers system operators a computationally efficient approach for monitoring system dynamics. However, the challenges of processing multisource spatial–temporal data from different measurement systems remain unsolved, thus resulting in potentially biased results. In addition, most existing data-driven dynamic security assessment methods that focus on state estimation/prediction overlook the fault location identification, which is important to real-time decision-making. To address the above limitations, an advanced online dynamic security assessment, which learns system dynamics and fault characteristics from multisource spatial–temporal data, is developed. Considering the challenge posed by different sampling rates and sensor numbers, global and local spatial–temporal data from various measurement systems are modeled as graphs with different numbers of nodes and edges. Then, two different sets of graph neural networks are customized to learn global and local spatial–temporal features, respectively. With the learned multisource spatial–temporal features, a Fourier neural operator-based dynamics trajectory predictor and a multilayer perceptron-based fault location identifier are developed for the advanced online dynamic security assessment. Case studies on the IEEE 39 bus system and the IEEE 118 bus system validate the effectiveness and efficiency of the developed online dynamic security assessment.