Abstract
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system opera-tors 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.
Original language | English |
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Title of host publication | Proceedings of 2021 IEEE Madrid PowerTech |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2021 |
Article number | 9495063 |
ISBN (Print) | 9781665435970 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE Madrid PowerTech - Virtual Event - from the Alberto Aguilera Campus of Comillas University, Madrid, Spain Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 14 https://www.powertech2021.com/ |
Conference
Conference | 2021 IEEE Madrid PowerTech |
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Number | 14 |
Location | Virtual Event - from the Alberto Aguilera Campus of Comillas University |
Country/Territory | Spain |
City | Madrid |
Period | 28/06/2021 → 02/07/2021 |
Internet address |
Keywords
- Physics-informed neural networks
- State estimation
- Swing equation
- System identification