Power system frequency monitoring and emergency control with neural ordinary differential equations

Increasing renewable energy supply and distributed generating sources in the power grid lead to lower inertia levels. Lower inertia combined with higher uncertainty in operation can cause drastic frequency fluctuations when a disturbance occurs. System operators must know whether the transmission system is secure against a disturbance. Real-time models attempt to predict the frequency in near-real time however require pretraining on a large variety of possible disturbances. Training models in real-time would not require pre-training as they are directly trained on the occurring disturbance. However, training in real-time is not feasible until now as fast-occurring system dynamics require shorter prediction (and training) times for security and operation which standard machine learning models are not capable of. For the first time, this work proposes a fast training strategy that learns Neural Ordinary Differential Equations (NODE) in near real-time directly on the occurring disturbance, simultaneously addressing the inaccuracy issue of model-based dynamic studies. NODE learns the dynamics or derivatives of an ODE system that standard ODE solvers can solve. NODE provides a continuous function for predicting future dynamics in a decentralized way, hence faster frequency stability assessment for longer time spans. We propose a collocation-based sampling using the collocation gradients. Case studies on the IEEE 39-bus system show the approach is feasible for near real-time operation, accurately predicts future system states, and enables operators to apply predesigned corrective control actions, potentially making the system secure for future disturbances.