Abstract
The deregulation of the power industry' in the United States along with the increasing demand for power may require the wheeling of large amounts of power between areas that are geographically distant from one another. This has necessitated the development of Wide Area Monitoring Systems (WAMS) that can provide a realtime picture of the state of the system. The d ata so acquired can be used for a variety of control and protection functions, all of which have a more reliable operation of the power system as the ultimate goal. Transmission line fault location and parameter estimation are two such functions. Most existing methods of fault location compute the phasor representation of a set of time-domain samples of the voltage and current from the transmission line, and then apply the phasor to locating the fault. This dissertation proposes and develops a method that solves the equations of the transmission line model in the time-domain directly. The method is tested using data generated from the simulation of a power system using the Electromagnetic Transients Program (EM TP). It is expected that the new method will locate faults more accurately than the phasor-based methods. The accuracy of the param eter values of the transmission line has a direct bearing on the accuracy of the final fault location. This dissertation also develops a method for on-line param eter estimation, using time-domain samples of voltages and currents. The estim ated parameters are then used in the fault location algorithm developed previously to accurately estimate the fault. The techno logy driving the development in the field of WAMS is based on the Global Positioning System of Satellites (GPS). Specialized data acquisition units, located at various points in the system continuously collect the relevant data and pass it along to one or many control centers. The GPS satellites are used to synchronize the system-wide measurements.
Gopalakrishnan, Ashok (2000). Fault location and parameter estimation on overhead transmission lines using synchronized sampling. Texas A&M University. Texas A&M University. Libraries. Available electronically from
https : / /hdl .handle .net /1969 .1 /DISSERTATIONS -1987285.