DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration

Abstract

Here, this letter proposes a novel Gaussian mixture model-based ensemble Kalman filter (GMM-EnKF) approach to the accurate calibration of the parameters of machine dynamic models. This approach aims to overcome some practical challenges affecting parameter calibration accuracy. Lastly, results show the proposed approach can provide precise calibrated parameters even when the machine operates under unbalanced network conditions with non-Gaussian measurement noises.

Authors:
 [1];  [2];  [3]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Electricity Infrastructure
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Global Energy Interconnection Research Institute North America, San Jose, CA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1457758
Report Number(s):
PNNL-SA-132149
Journal ID: ISSN 0885-8969
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Energy Conversion
Additional Journal Information:
Journal Volume: 33; Journal Issue: 3; Journal ID: ISSN 0885-8969
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 97 MATHEMATICS AND COMPUTING; Parameter calibration; Gaussian mixture model; ensemble Kalman filter; unbalanced network; non-Gaussian noises

Citation Formats

Fan, Rui, Huang, Renke, and Diao, Ruisheng. Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration. United States: N. p., 2018. Web. doi:10.1109/tec.2018.2849856.
Fan, Rui, Huang, Renke, & Diao, Ruisheng. Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration. United States. https://doi.org/10.1109/tec.2018.2849856
Fan, Rui, Huang, Renke, and Diao, Ruisheng. Fri . "Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration". United States. https://doi.org/10.1109/tec.2018.2849856. https://www.osti.gov/servlets/purl/1457758.
@article{osti_1457758,
title = {Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration},
author = {Fan, Rui and Huang, Renke and Diao, Ruisheng},
abstractNote = {Here, this letter proposes a novel Gaussian mixture model-based ensemble Kalman filter (GMM-EnKF) approach to the accurate calibration of the parameters of machine dynamic models. This approach aims to overcome some practical challenges affecting parameter calibration accuracy. Lastly, results show the proposed approach can provide precise calibrated parameters even when the machine operates under unbalanced network conditions with non-Gaussian measurement noises.},
doi = {10.1109/tec.2018.2849856},
journal = {IEEE Transactions on Energy Conversion},
number = 3,
volume = 33,
place = {United States},
year = {Fri Jun 22 00:00:00 EDT 2018},
month = {Fri Jun 22 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 10 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: DFIM under an unbalanced network with non-Gaussian noises

Save / Share:

Works referenced in this record:

Generator dynamic model validation and parameter calibration using phasor measurements at the point of connection
journal, May 2013

  • Huang, Zhenyu; Du, Pengwei; Kosterev, Dmitry
  • IEEE Transactions on Power Systems, Vol. 28, Issue 2, p. 1939-1949
  • DOI: 10.1109/TPWRS.2013.2251482

Practical Considerations to Calibrate Generator Model Parameters Using Phasor Measurements
journal, September 2017

  • Tsai, Chin-Chu; Chang-Chien, Le-Ren; Chen, I-Jen
  • IEEE Transactions on Smart Grid, Vol. 8, Issue 5
  • DOI: 10.1109/TSG.2016.2519528

Coordinated Control of DFIG's Rotor and Grid Side Converters During Network Unbalance
journal, May 2008


Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter
journal, May 2018


Current decomposition in asymmetrical, unbalanced three-phase systems under nonsinusoidal conditions
journal, January 1994

  • Cristaldi, L.; Ferrero, A.; Superti-Furga, G.
  • IEEE Transactions on Instrumentation and Measurement, Vol. 43, Issue 1
  • DOI: 10.1109/19.286356

Variable Projection Method for Power System Modal Identification
journal, November 2014

  • Borden, Alexander R.; Lesieutre, Bernard C.
  • IEEE Transactions on Power Systems, Vol. 29, Issue 6
  • DOI: 10.1109/TPWRS.2014.2309635

Comparison of Ensemble Kalman Filters under Non-Gaussianity
journal, April 2010

  • Lei, Jing; Bickel, Peter; Snyder, Chris
  • Monthly Weather Review, Vol. 138, Issue 4
  • DOI: 10.1175/2009MWR3133.1

Semiautomated Model Validation of Power Plant Equipment Using Online Measurements
journal, June 2013

  • Pourbeik, Pouyan; Rhinier, Randy; Hsu, Shih-Min
  • IEEE Transactions on Energy Conversion, Vol. 28, Issue 2
  • DOI: 10.1109/TEC.2013.2242074

Works referencing / citing this record:

A Multibranch Object Detection Method for Traffic Scenes
journal, November 2019

  • Feng, Jiangfan; Wang, Fanjie; Feng, Siqin
  • Computational Intelligence and Neuroscience, Vol. 2019
  • DOI: 10.1155/2019/3679203

A Multibranch Object Detection Method for Traffic Scenes
journal, November 2019

  • Feng, Jiangfan; Wang, Fanjie; Feng, Siqin
  • Computational Intelligence and Neuroscience, Vol. 2019
  • DOI: 10.1155/2019/3679203

Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter
journal, November 2019

  • Nan, Dongliang; Wang, Weiqing; Wang, Kaike
  • Applied Sciences, Vol. 9, Issue 23
  • DOI: 10.3390/app9235200

Figures / Tables found in this record:

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.