Prof. BU Siqi, Member of the Otto Poon Charitable Foundation Research Institute for Smart Energy (RISE), Associate Professor and Associate Head of the Department of Electrical and Electronic Engineering, and his colleagues recently published a research paper titled “Real-time Multi-stability Risk Assessment and Visualization of Power Systems: A Graph Neural Network-based Method” in IEEE Transactions on Power Systems.
The paper proposed a real-time multi-stability risk assessment (MSRA) method based on a graph neural network (GNN) for addressing multiple stability problems in renewable energy-integrated power systems in an effective manner, including rotor angle (small-disturbance and transient), voltage (short-term and long-term), frequency and converter-driven stability. The team employed a GraphNorm method to tackle over-smoothing problems and improve the generalisability of the GNN. The method proposed by the team can simultaneously and continuously predict the risks of multiple types of stability based on real-time data, and visualise stable and unstable operation regions (SURs) based on alpha shapes. The effectiveness of the proposed method has been verified in the IEEE 39-bus system, the 179-bus Western Electricity Coordinating Council (WECC) system, and the Great Britain (GB) system.
Full paper: https://ieeexplore.ieee.org/document/10819251/metrics#metrics
