Electrical transmission and distribution networks are prone to short circuit faults
since they span over long distances to deliver the electrical power from generating
units to where the energy is required. These faults are usually caused by vegetation
growing underneath bare overhead conductors, large birds short circuiting the
phases, mechanical failure of pin-type insulators or even insulation failure of cables
due to wear and tear, resulting in creepage current.
Short circuit faults are highly undesirable for distribution network companies since
they cause interruption of supply, thus affecting the reliability of their network,
leading to a loss of revenue for the companies. Therefore, accurate offline fault
location is required to quickly tackle the repair of permanent faults on the system so
as to improve system reliability. Moreover, it also provides a tool to identify weak
spots on the system following transient fault events such that these future potential
sources of system failure can be checked during preventive maintenance.
With these aims in mind, a novel fault location technique has been developed to
accurately determine the location of short circuit faults in a distribution network
consisting of feeders and spurs, using only the phase currents measured at the
outgoing end of the feeder in the substation. These phase currents are analysed using
the Discrete Wavelet Transform to identify distinct features for each type of fault. To
achieve better accuracy and success, the scheme firstly uses these distinct features to
train an Artificial Neural Network based algorithm to identify the type of fault on the
system. Another Artificial Neural Network based algorithm dedicated to this type of
fault then identifies the location of the fault on the feeder or spur. Finally, a series of
Artificial Neural Network based algorithms estimate the distance to the point of fault
along the feeder or spur.
The impact of wind farm connections consisting of doubly-fed induction generators
and permanent magnet synchronous generators on the accuracy of the developed
algorithms has also been investigated using detailed models of these wind turbine
generator types in Simulink. The results obtained showed that the developed scheme
allows the accurate location of the short circuit faults in an active distribution
network. Further sensitivity tests such as the change in fault inception angle, fault impedance, line length, wind farm capacity, network configuration and white noise confirm the robustness of the novel fault location technique in active distribution networks.
|Date of Award||30 Jun 2015|
|Supervisor||Raj Aggarwal (Supervisor)|
- fault location, artificial neural networks