Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods

Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods

It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system. Facing this problem, traditional methods, su...

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Bibliographic Details
Main Author: Xu, Yijun
Other Authors: Electrical Engineering
Format: Others
Published: Virginia Tech 2020
Subjects:
Uncertainty Quantification
Dynamic State Estimation
Generalized Polynomial Chaos
Multi-Element Polynomial Chaos
ANOVA
Polynomial-Chaos-Based Kalman Filter
Response Surface
Bayesian Inference
Markov Chain Monte Carlo.
Online Access:http://hdl.handle.net/10919/97876

Internet

http://hdl.handle.net/10919/97876

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