Polynomial-Chaos-Based Decentralized Dynamic Parameter Estimation Using Langevin MCMC

Simple item page
dc.contributor.authorXu, Yijunen
dc.contributor.authorChen, Xiaoen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorHuang, Canen
dc.contributor.authorKorkali, Merten
dc.date.accessioned2024-01-22T14:48:06Zen
dc.date.available2024-01-22T14:48:06Zen
dc.date.issued2019-08-01en
dc.description.abstractThis paper develops a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation. Under Bayesian inference framework, the non-Gaussian posterior distributions of the parameters can be obtained through Markov Chain Monte Carlo (MCMC). However, the latter method suffers from a prohibitive computing time for large-scale systems. To overcome this problem, we develop a decentralized generator model with the PCE-based surrogate, which allows us to efficiently estimate some generator parameter values. Furthermore, the gradient of the surrogate model can be easily obtained from the PCE coefficients. This allows us to use the gradient-based Langevin MCMC in lieu of the traditional Metropolis-Hasting algorithm so that the sample size can be greatly reduced. Simulations carried out on the New England system reveal that the proposed method can achieve a speedup factor of three orders of magnitude as compared to the traditional method without losing the accuracy.en
dc.description.versionPublished versionen
dc.format.extentPages 1-5en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/PESGM40551.2019.8973422en
dc.identifier.eissn1944-9933en
dc.identifier.isbn9781728119816en
dc.identifier.issn1944-9925en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117513en
dc.identifier.volume2019-Augusten
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titlePolynomial-Chaos-Based Decentralized Dynamic Parameter Estimation Using Langevin MCMCen
dc.title.serialIEEE Power and Energy Society General Meetingen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2019-08-08en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2019-08-04en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Polynomial-Chaos-Based_Decentralized_Dynamic_Parameter_Estimation_Using_Langevin_MCMC.pdf
Size:
375.2 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description:
Download

Collections

All Faculty Deposits
Scholarly Works, Electrical and Computer Engineering