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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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
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Summary: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-based approach is developed to identify the critical parameters from a nonlinear system with much better performance than the widely used trajectory sensitivity-based method. Then, synchrophasor measurement-based conditional variational autoencoder is developed to estimate the parameters' posterior distributions even in the presence of a high-dimensional case with eighteen critical parameters to be calibrated. The effectiveness of the proposed method is validated for a hydro generator with a very detailed model. The results show that the proposed approach can accurately and efficiently estimate the generator parameters' posterior distributions even when the parameters true values are not in support of the prior distribution.
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USDOE
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3107515