Basic goal of power system is to continuously provide electrical energy to the users.
Like with any other system, failures in power system can occur. In those situations it is
critical that correct remedial actions are applied as soon as possible after the accurate fault
condition and location are detected. This thesis has been focusing on automated fault
location procedure.
Different fault location algorithms, classified according to the spatial placement of
physical measurements on single ended, multiple ended and sparse system-wide, are
investigated. As outcome of this review, methods are listed as function of different
parameters that influence their accuracy. This comparison is than used for generating
procedure for optimal fault location algorithm selection. According to available data, and
position of the fault with respect to the data, proposed procedure decides between
different algorithms and selects an optimal one. A new approach is developed by utilizing
different data structures such as binary tree and serialization in order to efficiently
implement algorithm decision engine.
After accuracy of algorithms is strongly influenced by available input data, different
data sources are recommended in proposed architecture such as the digital fault
recorders, circuit breaker monitoring, SCADA, power system model and etc. Algorithm
for determining faulted section is proposed based on the data from circuit breaker
monitoring devices. This algorithm works in real time by recognizing to which sequence
of events newly obtained recording belongs.
Software prototype of the proposed automated fault location analysis is developed
using Java programming language. Fault location analysis is automatically triggered by
appearance of new event files in a specific folder. The tests were carried out using the real
life transmission system as an example.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2061 |
| Date | 15 May 2009 |
| Creators | Knezev, Maja |
| Contributors | Kezunovic, Mladen |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0025 seconds