Critical investigations on performance of ANN and wavelet fault classifiers

With increasing demands and competitive business environment, the structure of power system has become very complex. Moreover, power system is a dynamic framework due to faults and rapid load variations. Hence, the detection algorithms for faults are potential areas of research. To discuss this issue and to provide the solution methodology for detection of faults and further classification of those in a smart grid is a primary motivation of this manuscript. This paper presents application of supervised learning algorithms based on different neural network topologies for detection and classification of the faults in transmission lines in power system. Different wavelet transforms on different Multi Resolution Analysis levels are applied for detection of the potential features from the voltage waveforms of the Phasor Measurement Units (PMUs). These wavelet transforms are then applied to several neural networks classification engines to classify faults. Binary classification technique is used for definitions of faults. Different faults namely single line to ground, line to line, double line to ground and three phase symmetrical faults are designated as a binary digit. These definitions are employed to train the classification engine. Different plots of confusion and errors are plotted to establish a fair comparison between supervised learning algorithms.