Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This book presents an artificial neural network (ANN) modelling in detecting the PD signal. PD signals are generated from experimental laboratory and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis used the straight forward procedure in PD signal classification. Second analysis presents the de-noising of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). The result of this analysis shows ANN is the best de-noising technique compare to others.
Muzamir Isa was born in Malaysia in 1979. He received Doctoral (Ph.D) degree from Aalto University, Helsinki, Finland. His research interests are partial discharge measurement, detection and location technique, and power system transient studies including EMTP-ATP simulation.
Mohamad Nur Khairul Hafizi Rohani
Faizah Abu Bakar
Number of Pages:
LAP LAMBERT Academic Publishing
Partial Discharge, artificial neural network, Medium Voltage, High Voltage
SCIENCE / Electricity