Affiliation:
1. Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
2. Applied Mathematics and Plasmas Physics Division, Los Alamos National Laboratory, Los Alamos, NM 87544, USA
Abstract
The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.
Funder
US National Science Foundation
Reference40 articles.
1. Wang, R., Shi, L., Yao, L., and Ni, Y. (2014, January 27–31). Small signal stability analysis with high penetration of grid-connected wind farm of PMSG type considering the wake effect. Proceedings of the 2014 IEEE PES General Meeting|Conference & Exposition, National Harbor, MD, USA.
2. A review of machine learning approaches to power system security and stability;Alimi;IEEE Access,2020
3. Wang, R., Shi, L., Liu, Y., Luo, J., and Yao, L. (2015, January 26–29). A joint probabilistic analytical model with consideration of wind power and load uncertainties. Proceedings of the 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, China.
4. Modelling and solutions of coordinated economic dispatch with wind–hydro–thermal complex power source structure;Shi;IET Renew. Power Gener.,2017
5. Alsharief, Y. (2019). Transient Stability Simulation of Combined Three-Phase Unbalanced Transmission and Distribution Networks, Illinois Institute of Technology.