Physics-Guided Deep Neural Networks for Power Flow Analysis

Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics, and uncertainties, making tradition...

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Vydáno v:IEEE transactions on power systems Ročník 36; číslo 3; s. 2082 - 2092
Hlavní autoři: Hu, Xinyue, Hu, Haoji, Verma, Saurabh, Zhang, Zhi-Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Computational modeling
data-driven analysis
Electrical impedance
Knowledge engineering
Machine learning
Mathematical model
Mathematical models
Neural networks
Numerical models
Physics
physics-guided learning
Power flow
Power flow analysis
power flow solver
Power systems
System dynamics
Topology
ISSN:0885-8950, 1558-0679
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Shrnutí:Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics, and uncertainties, making traditional numerical approaches infeasible. To address these concerns, researchers have proposed data-driven approaches to solve the PF problem by learning the mapping rules from historical system operation data. Nevertheless, prior data-driven approaches suffer from poor performance, and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems. In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws, and system topology into the rebuilt PF model, our neural-network based PF solver is regularized by the auxiliary task, and constrained by the physical laws. The simulation results show that our physics-guided neural network methods achieve better performance, and generalizability compared to existing unconstrained data-driven approaches. Furthermore, we demonstrate that the weight matrices of the proposed neural networks embody power system physics by showing their similarities with the bus admittance matrices.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3029557