Degree Granting Department
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). Graduate Theses and Dissertations.