2025/12/08 by Wolf, Hannes M. H., Hans, Christian A.
Engineering · Physics and Astronomy · #Artificial neural network #Control theory (sociology) #Convolutional neural network #Differential (mechanical device) #Electric power system #FOS: Electrical engineering #Identification (biology) #Model Reduction and Neural Networks #Ordinary differential equation #Phase (matter) #Power System Optimization and Stability #Real-time simulation and control systems #System identification #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · open access · doi:10.48550/arxiv.2512.07757
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/12/08 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/28
Due the complexity of modern power systems, modeling based on first-order principles becomes increasingly difficult. As an alternative, dynamical models for simulation and control design can be obtained by black-box identification techniques. One such technique for the identification of continuous-time systems are neural ordinary differential equations. For training and inference, they require initial values of system states, such as phase angles and frequencies. While frequencies can typically be measured, phase angle measurements are usually not available. To tackle this problem, we propose a novel structure based on augmented neural ordinary differential equations, learning latent phase angle representations on historic observations with temporal convolutional networks. Our approach combines state-of-the art deep learning techniques, avoiding the necessity of phase angle information for the power system identification. Results show, that our approach clearly outperforms simpler augmentation techniques.