Transmissionlinesare thebackboneof electricalpower
systems and other power utilities as they are used for transmission
and distribution of power. Power is distributed to the end-user
through either overhead cables or underground cables. In the case
of underground cables, their propensity to fail in service
increases as they age with time. The increase in failure rates and
system crashes on older underground power cables now
negatively affect system reliability and involve numerous losses.
It is therefore easy to realize that the consequences of this trend
need to be managed [3]. Identification of the type of defects and
their locations along the length ofthe cablesis vital to minimize the
operating costs by reducing lengthy and expensive patrolsto locate
the faults, and to speed up repairs and restoration of power in the
lines. In this study, a method that combines wavelets and neurofuzzy techniques for faultlocation and identificationare proposed.
For this methodology a power transmission line model was
developed and different fault locations were simulated in
MATLAB/SIMULINK, and, as an input to the training and
development of the Adaptive Network Fuzzy Inference System
(ANFIS), certain selected features of the wavelet transformed
signals were used. Fault index obtained from wavelet
transformation are used as input variable for fuzzy input block
function. Different membership functions were observed within
input block function. As per formulation of rules, for
membership function, the output value of the defuzzifier
component was decoded to give a crisp value of ANFIS output.
ANFIS results were compared with actual values. A comparison
of the ANFIS output values and the actual values show that the
percentage error was less than 1%. Thus, it can be concluded that
the wavelet-ANFIS technique is accurate enough to be used in
identifying and locating underground power line faults. Which
will help in solving this time taking and tedious problem more
efficiently and thereby reducing human effort in finding the type
of fault and its location.
Fault identification in power cables by using travelling wave-based approach