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Title: An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors

Abstract

Polynomial-chaos-expansion-based surrogate models have recently been advocated in the literature for power system dynamic parameter estimation. Regarding the estimation of the uncertain generator parameters, a Bayesian inference framework has been proposed based on a polynomial-based reduced-order representation of the synchronous machines using assumed parameter values. Then, the non-Gaussian posterior probability distribution functions (pdfs) of these parameters are recovered through the stochastic sampling approach efficiently. However, facing very large parameter errors, the reliability of the surrogate model decreases, yielding biased estimation results. To overcome this problem, here we develop hierarchical Bayesian inference framework that processes the measurements provided by phasor measurement units, while making use of multifidelity surrogates together with the importance sampling method. The latter allows us to estimate in an efficient manner the posterior pdfs of the uncertain parameters through the normalized weights of the prior samples. To improve the accuracy of the posterior pdfs, an adaptive procedure is further adopted in the importance sampling for the gradual evolution of its proposal functions. The new proposals assist in fine-tuning the sample space and thereby help to construct surrogates with higher fidelity. Through an iterative process, this approach is able to estimate accurately and efficiently non-Gaussian posterior pdfs of themore » uncertain generator parameters subject to gross errors.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Falls Church, VA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE)
OSTI Identifier:
1668497
Report Number(s):
LLNL-JRNL-778500
Journal ID: ISSN 1551-3203; 971851
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Industrial Informatics
Additional Journal Information:
Journal Volume: 16; Journal Issue: 8; Journal ID: ISSN 1551-3203
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
generator parameter estimation; polynomial chaos expansion; Bayesian approach; importance sampling (IS); multifidelity surrogate

Citation Formats

Xu, Yijun, Mili, Lamine, Korkali, Mert, and Chen, Xiao. An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors. United States: N. p., 2019. Web. doi:10.1109/tii.2019.2950238.
Xu, Yijun, Mili, Lamine, Korkali, Mert, & Chen, Xiao. An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors. United States. doi:10.1109/tii.2019.2950238.
Xu, Yijun, Mili, Lamine, Korkali, Mert, and Chen, Xiao. Tue . "An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors". United States. doi:10.1109/tii.2019.2950238. https://www.osti.gov/servlets/purl/1668497.
@article{osti_1668497,
title = {An Adaptive Bayesian Parameter Estimation of a Synchronous Generator Under Gross Errors},
author = {Xu, Yijun and Mili, Lamine and Korkali, Mert and Chen, Xiao},
abstractNote = {Polynomial-chaos-expansion-based surrogate models have recently been advocated in the literature for power system dynamic parameter estimation. Regarding the estimation of the uncertain generator parameters, a Bayesian inference framework has been proposed based on a polynomial-based reduced-order representation of the synchronous machines using assumed parameter values. Then, the non-Gaussian posterior probability distribution functions (pdfs) of these parameters are recovered through the stochastic sampling approach efficiently. However, facing very large parameter errors, the reliability of the surrogate model decreases, yielding biased estimation results. To overcome this problem, here we develop hierarchical Bayesian inference framework that processes the measurements provided by phasor measurement units, while making use of multifidelity surrogates together with the importance sampling method. The latter allows us to estimate in an efficient manner the posterior pdfs of the uncertain parameters through the normalized weights of the prior samples. To improve the accuracy of the posterior pdfs, an adaptive procedure is further adopted in the importance sampling for the gradual evolution of its proposal functions. The new proposals assist in fine-tuning the sample space and thereby help to construct surrogates with higher fidelity. Through an iterative process, this approach is able to estimate accurately and efficiently non-Gaussian posterior pdfs of the uncertain generator parameters subject to gross errors.},
doi = {10.1109/tii.2019.2950238},
journal = {IEEE Transactions on Industrial Informatics},
issn = {1551-3203},
number = 8,
volume = 16,
place = {United States},
year = {2019},
month = {10}
}

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