Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration
Journal Article
·
· IEEE Transactions on Energy Conversion
- 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)
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.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1457758
- Report Number(s):
- PNNL-SA-132149
- Journal Information:
- IEEE Transactions on Energy Conversion, Vol. 33, Issue 3; ISSN 0885-8969
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 10 works
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