Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
| Autor: | Spyros Chatzivasileiadis, George S. Misyris, Jochen Stiasny |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Artificial neural network Estimation theory Phasor System identification Control engineering Kalman filter Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Electric power system Units of measurement Test case FOS: Electrical engineering electronic engineering information engineering Physics-informed neural networks State estimation Swing equation |
| Zdroj: | Stiasny, J, Misyris, G S & Chatzivasileiadis, S 2021, Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics . in Proceedings of 2021 IEEE Madrid PowerTech ., 9495063, IEEE, 2021 IEEE Madrid PowerTech, Madrid, Spain, 28/06/2021 . https://doi.org/10.1109/PowerTech46648.2021.9495063 |
| Popis: | Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential to address challenges such as the stronger non-linearities of low-inertia systems, increased measurement noise, and limited availability of data. The estimator is demonstrated in several test cases using a 4-bus system, and compared with state of the art algorithms, such as the Unscented Kalman Filter (UKF), to assess its performance. 6 pages, 8 figures, accepted at IEEE PES PowerTech 2021 Madrid |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|