Short Circuit Fault Classification and Lo cation in Transmission Lines Using A Combination of Wavelet Transform and Support Vector Machines

In this paper, a modern synthetic framework which has the capability to rap- idly Classify and locate short circuit faults over transmission lines is presented. The proposed algorithm singles out short circuit faults based on the measured voltage wave- form and three-phase current when fault events occur in power transmission lines. The values resulting from the three-phase currents and the three-phase voltages wavelet transform are used to fault classification algorithm. Then, fault location algorithm is ac- tivated as the result of fault classification. Different kinds of methods such as multilevel wavelet transform and support vector machine have been combined in a set to determine fault classification and location at every time. This paper lays out the fundamental con- cept of the proposed framework and introduces a pattern recognition approach via wavelet transform, statistical processing techniques, neural network (NN) and a joint decision-making mechanism. Voltage and the recorded current values in measurement devices from the fault moment to a quarter of post-fault have been used for pre- processing and training in the support vectors machines.