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:
-
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Electricity Infrastructure
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- 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}
}
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Cited by: 10 works
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Figures / Tables:
Fig. 1: DFIM under an unbalanced network with non-Gaussian noises
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