Year of Publication

2021

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Engineering

Department/School/Program

Electrical and Computer Engineering

First Advisor

Dr. Yuan Liao

Abstract

Transmission line and load model parameters are essential inputs to power system modeling and simulation, control, protection, operation, optimization, and planning. These parameters usually vary over time or under different operating conditions. Thus, reliable estimation methods are desired to ensure the accuracy of those parameters. This research focuses on estimation for transmission line parameters and the ZIP load model. The proposed estimation methods can use both online measurements and historical data of a specified duration. The parameters of long transmission lines with different series-compensation configurations are estimated using linear methods and optimal estimators with bad data detection capability. Additionally, Kalman filter estimation methods have been proposed to improve the estimation accuracy and to track the dynamically changing line parameters under the effect of measurement noises. The estimation methods are tested with data generated using Matlab Simulink. For the ZIP load model parameter estimation, theoretical formulation for the aggregate ZIP load model has been established. The least squares, optimization, neural network, and Kalman filter methods have been investigated to estimate ZIP parameters and been verified based on OpenDSS simulation data.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2021.435

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