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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 Lab. (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. doi: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. doi: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 = {2018},
month = {6}
}

Journal Article:
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Cited by: 1 work
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Figures / Tables:

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

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