Applications of Physics-Informed Neural Networks in Power Systems - A Review

The advances of deep learning (DL) techniques bring new opportunities to numerous intractable tasks in power systems (PSs). Nevertheless, the extension of the application of DL in the domain of PSs has encountered challenges, e.g., high requirement for the quality and quantity of training data, prod...

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Bibliographic Details
Published in:	IEEE transactions on power systems Vol. 38; no. 1; pp. 572 - 588
Main Authors:	Huang, Bin, Wang, Jianhui
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anomalies Data models Deep learning Domains Engineering first principle Mathematical models Neural networks Optimization Parameter estimation Physics physics-informed neural networks Power flow smart grids State of the art Training Training data
ISSN:	0885-8950, 1558-0679
Online Access:	Get full text
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