Graduation Year


Document Type




Degree Granting Department

Electrical Engineering

Major Professor

Lingling Fan


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.