Abstract:
Transmission lines are the backbone of electrical power systems and other power
utilities as they are used for transmission and distribution of power. Power is dis-
tributed 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 breakdowns on
older underground power cables are now adversely impacting system reliability
and many losses involved. Therefore it is readily apparent that necessary action
has to be taken to manage the consequences of this trend. At any given length of
a cable, its deterioration or indication of failure manifests itself through discrete
defects. Identi cation of the type of defects and their locations along the length
of the cables is vital in order to minimize the operating costs by reducing lengthy
and expensive patrols to locate the faults, and to speed up repairs and restoration
of power in the lines. In this study, a method that combines wavelets and neuro-
fuzzy technique for fault location and identi cation is proposed. A 10km, 34.5KV,
50Hz power transmission line model was developed and di erent faults and loca-
tions simulated in MATLAB/SIMULINK, and then certain selected features of
the wavelet transformed signals were used as inputs for training and development
of the Adaptive Network Fuzzy Inference System (ANFIS). The results obtained
from ANFIS output were compared with the actual values. 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.
Keywords: ANFIS, Discrete wavelet transform (DWT), Fault location, Fault
types, and Underground cables
