To keep pace with increasing renewable energy penetration and consequent increase in inverter-based resources in the power grid, it is pertinent for present-day research to address the resulting drop in system inertia levels and its impact on frequency stability. With decreasing levels of inherent rotational inertia present in the system, any sudden disturbance causing an energy imbalance in the grid could lead to more drastic excursions of system frequency than those experienced hitherto. To ensure the resilience of the grid in such scenarios, advanced and competent frequency stability assessment and control methods are required. This thesis presents Neural Ordinary Differential Equations (NODE), a recently introduced family of neural networks, as an effective tool to achieve fast, real time estimates of the expected frequency response trajectory during an energy imbalance event.

Since high-impact frequency instability events are sparse in reality, both real-world grid data and synthetically generated data corresponding to different inertial conditions are used to train predictive NODE models. Firstly, NODE is adapted to frequency prediction applications through relevant data processing steps, and modification of network parameters and algorithmic aspects pertaining to the predictive model definition. Secondly, patterns corresponding to specific sections of the frequency response curve are used to selectively train NODE models. Pattern-specific training methods exhibit better prediction performance when the NODE model encounters frequency behaviour similar to the one it initially trained on. Thirdly, a pre-training approach to cut short on the real-time training time required by NODE models to achieve desired levels of prediction performance is presented. Fast estimates of critical frequency stability parameters like nadir could act as potential triggers for early stability control actions to achieve a more controlled frequency response.

Application of predictive NODE models for different frequency scenarios are presented using three test-cases: normal operating scenario, restoration post-system split scenario and synthetically generated high-impact frequency disturbance scenarios. Model tuning and training methods specific to each test-case are described, and prediction results are evaluated with relevant performance metrics. Finally, a comparison is made between the implementation of NODE among different test-cases and real-world implications of the frequency prediction outcomes from the test-cases are further discussed.