Fault Detection and Classification in Transmission Line Using FPGA

This paper presents a hardware efficient logic for fault detection and classification in transmission line using wavelet transformation technique, kNN algorithm and implemented using a field-programmable gate array (FPGA). The general SPARTAN3E FPGA board was employed for prototype development. All the coding done by using hardware description language called very high speed integrated circuit (VHDL). The alternating current signal samples are inputs to the system and is based on wavelet analysis. Since fault is associated with high frequency transients from current signals, depending on amount of high frequency components in current signals the faults are classified. MATLAB was used to apply the current signal input samples to the prototype. An adaptive threshold technology was used rather than fixed threshold in order to make classification more accurate and reliable using k NN algorithm or Standard deviation technique. The output of the proposed logic was shown in MODEL-SIM Software. A high level of computational efficiency is achieved in this algorithm as compared to the other wavelet based algorithms as only the high frequency details at first level are employed.