International Journal of Business and Research (IJBER) ISSN: 0975-0479 (www.ijber.com ) Volume 8, 2014 NEURAL NETWORK BASED FAULT DETECTION & CLASSIFICATION FOR TRANSMISSION LINES Ankit Jain 1 ,Neetu Agarwal 2 ,Gaurav Gangil 3 Jainankit066@gmail.com 1 , Neetuagarwal02@gmail.com 2 gauravmits2011@gmail.com 3 Vikrant institute of technology & science Gwalior 1,2 , Sobhasaria engineering college Sikar 3 ABSTRACT:- This Paper focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances. KEY WORDS: Artificial Neural Networks, Feedforward networks, Backpropagation Algorithm, Levenberg-Marquardt algorithm. INTRODUCTION One of the most important factors that hinder the continuous supply of electricity and power is a fault in the power system [2]. Any abnormal flow of current in a power system’s components is called a fault in the power system. These faults cannot be completely avoided since a portion of these faults also occur due to natural reasons which are way beyond the control of mankind. Hence, it is very important to have a well coordinated protection system that detects any kind of abnormal flow of current in the power system, identifies the type of fault and then accurately locates the position of the fault in the power system. The faults are usually taken care of by devices that detect the occurrence of a fault and eventually isolate the faulted section from the rest of the power system. Hence some of the important challenges for the incessant supply of power are detection, classification and location of faults [3]. Faults can be of various types namely Transient, persistent, symmetric or asymmetric faults and the fault detection process for each of these faults is distinctly unique in the sense, there is no one universal fault location technique for all these kinds of faults. From quite a few years, intelligent based methods are being used in the process of fault detection and location. Three major artificial intelligence based techniques that have been widely used in the power and automation industry are [6]: 1 Expert System Techniques 2 Artificial Neural Networks 3 Fuzzy Logic Systems Among these available techniques, Artificial Neural Networks (ANN) have been used extensively in this thesis for fault location on electric power transmission lines. These ANN based methods do not require a knowledge base for the location of faults unlike the other artificial intelligence based methods [7] ARTIFICIAL NEURAL NETWORK INTRODUCTION An Artificial Neural Network (ANN) can be described as a set of elementary neurons that are usually connected in biologically inspired architectures and organized in several layers [9]. The structure of a feed-forward ANN, also called as the perceptron. There are N i numbers of neurons in each i th layer and the inputs to these neurons are connected to the previous layer neurons. The input layer is fed with the excitation signals. Simply put, an elementary neuron is like a processor that produces an output by performing a simple non-linear operation on its inputs. A weight is attached to each and every neuron and training an ANN is the process of adjusting different weights tailored to the training set. An Artificial Neural Network learns to produce a response based on the inputs given by adjusting the node weights. Hence we need a set of data referred to as the training data set, which is used to train the neural network.