Yin, R;
Varga, L;
Cremen, G;
(2025)
Mitigating Risks in Renewable Energy Power Systems: Predicting Transient Voltage Angles with Physics-Informed Neural Networks.
In:
2024 8th International Conference on System Reliability and Safety (ICSRS).
(pp. pp. 133-137).
IEEE: Sicily, Italy.
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Abstract
As the penetration of renewable energy in global power systems continues to increase, new challenges to the stability and reliability of these systems have emerged. This study focuses on the dynamic impact of wind power generation's uncertainty and variability on power systems under gusty conditions characterized by significant short-term wind speed fluctuations. This study employs Physics-Informed Neural Networks (PINNs) to predict transient voltage angle variations in power systems subjected to wind power generation uncertainty. Using the IEEE 9 BUS system as a case study, voltage angle variations are generated through the swing equation and an ODE solver, serving as training data for the PINNs. The results demonstrate that PINNs effectively predicts transient voltage angles in unseen wind power datasets with only 20 training wind scenarios dataset. Overall, this paper illustrates the potential of PINNs in handling the complex dynamics of renewable energy-integrated power systems, particularly in forecasting and managing grid fluctuations caused by wind power generation.
| Type: | Proceedings paper |
|---|---|
| Title: | Mitigating Risks in Renewable Energy Power Systems: Predicting Transient Voltage Angles with Physics-Informed Neural Networks |
| Event: | 2024 8th International Conference on System Reliability and Safety (ICSRS) |
| Dates: | 20 Nov 2024 - 22 Nov 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICSRS63046.2024.10927507 |
| Publisher version: | https://doi.org/10.1109/icsrs63046.2024.10927507 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Renewable energy sources, Fluctuations, Uncertainty, Power system dynamics, Neural networks, Power system stability, Wind power generation, Mathematical models, Data models, Transient analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209921 |
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