Physics-guided Deep Learning for Power System State Estimation
- Univ. of Central Florida, Orlando, FL (United States)
- Clemson Univ., SC (United States)
In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.
- Research Organization:
- Univ. of Central Florida, Orlando, FL (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Grant/Contract Number:
- EE0007998
- OSTI ID:
- 1825970
- Journal Information:
- Journal of Modern Power Systems and Clean Energy, Vol. 8, Issue 4; ISSN 2196-5625
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks
Spatio-Temporal Deep Graph Network for Event Detection, Localization, and Classification in Cyber-Physical Electric Distribution System