Power Plant Model Parameter Calibration Using Conditional Variational Autoencoder

Accurate models of power plants play an important role in maintaining the reliable and secure grid operations. In this paper, we propose a synchrophasor measurement-based generator parameter calibration method by a novel deep learning method with high computational efficiency. An elementary effects-...

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Veröffentlicht in:	IEEE transactions on power systems Jg. 37; H. 2; S. 1642 - 1652
Hauptverfasser:	Khazeiynasab, Seyyed Rashid, Zhao, Junbo, Batarseh, Issa, Tan, Bendong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Calibration Computational modeling Conditional variational autoencoders Data models deep learning elementary effects Engineering Generators Mathematical model Mathematical models Nonlinear systems parameter estimation Parameter identification Phasor measurement units Power plants power system dynamics Power system stability synchrophasor measurements
ISSN:	0885-8950, 1558-0679
Online-Zugang:	Volltext
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