A novel approach for the detection, classification and localization of transmission lines faults using wavelet transform and Support Vector Machines classifier

Abstract

Over the time, techniques have been developed whose approach has been linked mainly to the diagnosis of faults, which is why over the years these techniques have been improved little by little in order to complement or at best cases innovate the traditional methods used for the detection of faults from the mathematical point of view relying mainly on sophisticated methods and some of them related to artificial intelligence. Taking into account the aforementioned, in this paper we propose the use one of the branches of artificial intelligence, specifically automatic learning through the tool known as Support Vector Machines (SVM) to find a method with which is feasible to identify and classify the type of fault. For the creation of the mathematical model it is essential to have a database. The database consists of input data and output data, the input data are the detail coefficients obtained from the decomposition of the current and voltage signals using the Discrete Wavelet Transform (DWT). Meanwhile, the output data are the labels assigned and with which the model can identify and classify the different types of faults. Both current signals and voltage signals are generated based on an extensive simulation of faults along the longest transmission line that has a test system.