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-...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on power systems Vol. 37; no. 2; pp. 1642 - 1652
Main Authors:	Khazeiynasab, Seyyed Rashid, Zhao, Junbo, Batarseh, Issa, Tan, Bendong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!