Date of Award


Degree Name

Doctor of Philosophy


Electrical and Computer Engineering

First Advisor

Dr. Johnson A. Asumadu

Second Advisor

Dr. Massood Z. Atashbar

Third Advisor

Dr. James Kamman


Kalman filter, noise covariance matrices, adaptive Kalman filter, state covariance matrix


The exact values of the noise covariance matrix of the Kalman filter state vector Q and the measured signal noise covariance matrix R must be obtained in order to achieve the optimal performance of the Kalman filter. There have been many techniques and assumptions made to evaluate and compute Q and R. The effects of Q and R are investigated in detail in this dissertation. Based on these investigations, the Kalman filter has been modeled to detect the fundamental signal amplitude variations of power system signals. This technique helps in evaluating voltage sags in power systems.

Two algorithms are proposed in this dissertation. In these algorithms, the performance of the Kalman filter is investigated with the assumption that Q and R matrices are unknown and not necessary to evaluate them exactly. In the proposed algorithms, two adaptive Kalman filters are used to detect and to track the variations in the fundamental amplitude of the measured power system voltage signal in order to diagnose voltage sags. Also, the algorithms work for normal frequency variations in power systems.

Access Setting

Dissertation-Open Access