الفهرس 
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Abstract
This thesis introduces a complete transmission line protection scheme
using synchronized sampling technique implemented with neural networks
to precisely detect, classify and locate fault over along transmission line.
For fault detection and classification module, synchronized data of voltage
and current signals from both ends of transmission line are processed using
a mathematical algorithm to generate the inputs to an Artificial Neural
Network (ANN). The target of that ANN signifies the occurrence of a fault.
The appearance of current featured modal components points out the type
of fault. A data window of 0.25 cycle is used to ensure a fast process.
For the fault location, two approaches were adopted, both based on
synchronized sampling time domain data:
Single stage Featured Fault currents fault locator: The first approach uses
the featured fault modal components chosen according to fault type as an
input to ANN. The ANN is trained on different system conditions (fault
inception angle, system loading, fault resistance, fault type) while the ANN
target points out the fault location with an acceptable margin.
Single Stage Current Traveling wave based fault locators: The second
approach on a different mathematical formulation. The algorithm was used
to calculate fault location using current traveling waves based on
synchronized data (Single Stage).