Graduation Year
2012
Document Type
Dissertation
Degree
Ph.D.
Degree Granting Department
Electrical Engineering
Major Professor
Lingling Fan
Abstract
This dissertation tackles the online estimation of synchronous machines' power subsystems electromechanical models using the output based Phasor Measurements Units (PMUs) data while disregarding any inside data. The research develops state space models
and estimates their parameters and states. The research tests the developed algorithms against models of a higher and of the same complexity as the estimated models.
The dissertation explores two estimations approaches using the PMUs data: i)non-linear Kalman filters namely the Extended Kalman Filter (EKF) and then the Unscented
Kalman Filter (UKF) and ii) Least Squares Estimation (LSE) with Finite Differences (FN) and then with System Identification. The EKF based research i) establishes a decoupling
technique for the subsystem the rest of the power system ii) finds the maximum number of parameters to estimate for classical machine model and iii) estimates such parameters
. The UKF based research i) estimates a set of electromechanical parameters and states for the flux decay model and ii) shows the advantage of using a dual estimation filter with
colored noise to solve the difficulty of some simultaneous state and parameter estimation.
The LSE with FN estimation i) evaluates numerically the state space differential equations and transform the problem to an overestimated linear system whose parameters
can be estimated, ii) carries out sensitivity studies evaluating the impact of operating conditions and iii) addresses the requirements for implementation on real data taken from
the electric grid of the United States. The System Identification method i) develops a linearized electromechanical model, ii) completes a parameters sub-set selection study using
si8ngular values decomposition, iii) estimates the parameters of the proposed model and iv) validates its output versus the measured output.
Scholar Commons Citation
Wehbe, Yasser, "Model Estimation of Electric Power Systems by Phasor Measurement Units Data" (2012). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/4419
