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

Journal Article · · IEEE Transactions on Energy Conversion
 [1];  [1];  [2]
  1. BATTELLE (PACIFIC NW LAB)
  2. Global Energy Interconnection Research Institute North America

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 of machine parameter calibration. Results show the proposed approach can provide high-accuracy calibrated parameters even when the machine operates under unbalanced network conditions and the measurement noises are non-Gaussian.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
1511578
Report Number(s):
PNNL-SA-132149
Journal Information:
IEEE Transactions on Energy Conversion, Vol. 33, Issue 3
Country of Publication:
United States
Language:
English

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Coordinated Control of DFIG's Rotor and Grid Side Converters During Network Unbalance May 2008
Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter May 2018
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Variable Projection Method for Power System Modal Identification November 2014
Comparison of Ensemble Kalman Filters under Non-Gaussianity April 2010
Semiautomated Model Validation of Power Plant Equipment Using Online Measurements June 2013

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