Publication: Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable
1
0
Issued Date
2011
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-79960574280
Rights Holder(s)
Scopus
Bibliographic Citation
IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011. Vol 2, No. (2011), p.996-1000
Suggested Citation
Kaitwanidvilai S., Pothisarn C., Jettanasen C., Chiradeja P., Ngaopitakkul A. Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable. IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011. Vol 2, No. (2011), p.996-1000. Retrieved from: https://hdl.handle.net/20.500.14740/7283
Abstract
This paper proposes a new technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for fault classifications on underground cable. Simulations and the training process for the back-propagation neural network are performed using ATP/EMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The results are shown that an average accuracy values obtained from BPNN can indicate the fault classification with satisfactory accuracy, and will be very useful in the development of a power system protection scheme.
Subject(s)
ATP/EMTP
Back propagation neural networks
Decision algorithms
Electricity distribution
Fault classification
High frequency components
Mother wavelets
Power system protection
Sequence current
Thailand
Training patterns
Training process
Underground systems
Backpropagation algorithms
Cables
Computer science
Computer simulation
Discrete wavelet transforms
Electric utilities
Engineers
Fault detection
MATLAB
Signal detection
Torsional stress
Underground cables
Neural networks
Back propagation neural networks
Decision algorithms
Electricity distribution
Fault classification
High frequency components
Mother wavelets
Power system protection
Sequence current
Thailand
Training patterns
Training process
Underground systems
Backpropagation algorithms
Cables
Computer science
Computer simulation
Discrete wavelet transforms
Electric utilities
Engineers
Fault detection
MATLAB
Signal detection
Torsional stress
Underground cables
Neural networks