Bayesian Inference for Thermal Model of Synchronous Generator Part-II: State Estimation

This paper is the Part II of the series on parameter and state estimation using the Bayesian inference for a thermal model of a synchronous generator. Part I is about Parameter Estimation. In this paper, state estimation of rotor copper and air-gap temperatures of the synchronous generator are perfo...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.105612-105620
Main Authors: Pandey, Madhusudhan, Lie, Bernt
Format: Article
Language:English
Subjects:
Accuracy
Air gaps
Algorithms
Atmospheric measurements
Bayes methods
Bayesian analysis
Bayesian method
Computational efficiency
Computing time
EnKF
Estimators
Kalman filters
Mathematical models
Parameter estimation
Particle filters
Particle measurements
State estimation
Statistical inference
synchronous generator
Synchronous generators
Thermal analysis
thermal model
UKF
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Summary:This paper is the Part II of the series on parameter and state estimation using the Bayesian inference for a thermal model of a synchronous generator. Part I is about Parameter Estimation. In this paper, state estimation of rotor copper and air-gap temperatures of the synchronous generator are performed using Bayesian inference. Estimators such as Unscented Kalman Filter (UKF), Ensemble Kalman Filter (EnKF), and Particle Filters (PFs) with different sampling algorithms, are compared based on the estimation accuracy and computational time. The inferences are drawn for the posterior distributions of the state, dispersion of particles, error convergence and particle realizations of the state estimator, choice, and the computational effort of the estimators. Results show that UKF has fair estimation accuracy with the fastest computational time as compared to other estimators.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3209695