Recognition of Power System Transients and Fault Analysis based Discrete Wavelet Transform and Neural Network

Abdulhamid A. Abohagar and Mohd W. Mustafa


Power System Stability, Fault Analysis, High Impedance Fault, Neural Network


Now days, because of the great extent of capital investment to generate electrical power supply, power supply has become as an article of trade. It is extremely significant to serve the power in reliable and stable need to ensure customers requirement. To attain stability and to ensure power quality performance it is very important to identify power system faults. High impedance fault (HIF) is one of the various fault causes; distinctive characteristics of this type of faults are asymmetry and nonlinearly behavior. Arc which is usually associated with these kinds of faults is considered as a source of human life risks and fire hazardous. Hence, detection and protection of such faults still remain a topic of study. In this paper PSCAD/EMTDC simulation, software is used to simulate the high-voltage transmission system and modeling of HIF. MATLAB software is used for; advanced signal processing tools such as Discrete wavelet transform (DWT) which is used as feature extraction to get an useful information from faulted signal, back propagation neural network (BP-NN) for detection of high impedance fault.

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