Fault Detection on transmission lines forms an important part of monitoring the
health of the power plant and is an indicator of when potential faults can lead
to catastrophic failure of equipment. This research analyses the early detection of
generator, transmission line faults and also researches methods of fault detection via
the application of Artificial Neural Network techniques.
The monitoring of the generator voltages and currents, of transmission line performance
parameters forms an important monitoring criterion of large power systems.
Failures lead to system down time, damage to equipment and it presents a high risk
to the integrity of the power system, and affects the operability and reliability of
the network.
This dissertation therefore deals with fault detection on the Eskom transmission
lines from a simulation perspective. Electrical faults have always been a constant source of conflict between transmission lines and power consumers.
This dissertation presents the application faults detection on the transmission lines
using Artificial Neural Networks. The ANN is used to model and to predict the
occurrence of a transmission line fault, and classifies faults according to its transient
characteristics. Results show that the ANN can be used to accurately identify and
to classify faults, given accurate problem set-up and training. The major contribution of the dissertation is the application of ANNs to predict
faults on the transmission lines / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/21943 |
| Date | 24 January 2017 |
| Creators | Nonyane, Phillemon |
| Contributors | Boesack, Craig D., Du, Shengzhi |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (70 leaves) : illustrations |
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