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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Vydané v:	IEEE transactions on power systems Ročník 37; číslo 2; s. 1642 - 1652
Hlavní autori:	Khazeiynasab, Seyyed Rashid, Zhao, Junbo, Batarseh, Issa, Tan, Bendong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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. Not provided.
Author	Khazeiynasab, Seyyed Rashid Tan, Bendong Batarseh, Issa Zhao, Junbo
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SubjectTerms	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
Title	Power Plant Model Parameter Calibration Using Conditional Variational Autoencoder
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