Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

Abstract

The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.

A Review of Fault Diagnosing Methods in Power Transmission Systems

“…ANN data is trained by detailed coefficients obtained by DWT which are then employed for the Levenberg Marquardt algorithm to locate fault [135]. In [136], fundamental components of voltage and current signals are extracted by DFT. Different modular ANNs are employed and triplet vectors served as input for them.…”

An accurate fault location algorithm for transmission lines with use of two-end unsynchronized measurements

“…The transmission line has been represented by distributed parameter of one line model using Power System toolbox of Matlab software and the frequency dependence of the line parameters is taken into account. Complete system parameters are given in [1], [9]. The phase current and voltage signals extracted from the simulation at the relay location are processed with an antialiasing filter in order to filter the higher order harmonics.…”

“…Intelligent techniques have also been drawn on for fault location in transmission lines. In this framework many fault location approaches based on artificial intelligence techniques such as fuzzy logic [5], [6], adaptive neuro-fuzzy inference system (ANFIS) [7], [8], and neural networks [1], [9][10] have been investigated successfully.…”

A correlation coefficient‐based algorithm for fault detection and classification in a power transmission line

“…Techniques such as artificial neural network, fuzzy logic, and genetic algorithm (GA) are used widely in fault analysis of power transmission lines. For example, methods that employ different ANN architectures have been discussed . A continuous GA has also been used for fault section estimation .…”