الفهرس 
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Abstract
High impedance faults (HIFs) are a persistent problem on distribution systems because these faults do not draw sufficient fault current to be identified by conventional overcuirrent devices, mean while they may pose a serious hazard in the system.
Many researches had been performed in a trial to the characterization of these faults and the development of means to detect them.
This thesis utilizes the discrete wavelet transform (DWT) as well as the artificial neural network (ANN) to detect and classify faults.
First, extensive simulation studies are carried out on two systems at 11 kV and at 35 kV using the electromagnetic transient program (EMTP).
HIFs, low impedance faults (LIFs), load switching and capacitor bank switching are simulated by EMTP and their voltage signals are studied.
With the time-frequency localization embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. The output of the wavelet transform is then fed to to ANN.
Results demonstrate that this methodology is very promising for being used to discriminate the high impedance fault, the low impedance fault, the load switching and the capacitor switching.