Introduction
An overhead transmission line is one of the main components in every electric power system. The transmission line is exposed to the environment and the possibility of experiencing faults on the transmission line is generally higher than that on other main components. Line faults are the most common faults, they may be triggered by lightning strokes, trees may fall across lines, fog and salt spray on dirty insulators may cause the insulator strings to flash over, and ice and snow loadings may cause insulator strings to fail mechanically. When a fault occurs on an electrical transmission line, it is very important to detect it and to find its location in order to make necessary repairs and to restore power as soon as possible. The time needed to determine the fault point along the line will affect the quality of the power delivery. Therefore, an accurate fault location on the line is an important requirement for a permanent fault. Pointing to a weak spot, it is also helpful for a transient fault, which may result from a marginally contaminated insulator, or a swaying or growing tree under the line.
Fault location in transmission lines has been a subject of interest for many years. During the last decade a number of fault location algorithms have been developed, including the steady-state phasor approach, the differential equation approach and the traveling-wave approach (Lian and Salama, 1994), as well as two-end (Sheng and Elangovan, 1998) and one-end (Zhang et al., 1999) algorithms.
In the last category, synchronized (Kezunovic and Mrkic, 1994) and non-synchronized (Novosel et al., 1996) sampling techniques are used. However, two-terminal data are not widely available. From a practical viewpoint, it is desirable for equipment to use only one-terminal data. The one-end algorithms, in turn, utilize different assumptions to replace the remote end measurements. Most of fault locators are only based on local measurements. Currently, the most widely used method of overhead line fault location is to determine the apparent reactance of the line during the time that the fault current is flowing and to convert the ohmic result into a distance based on the parameters of the line.
It is widely recognized that this method is subject to errors when the fault resistance is high and the line is fed from both ends, and when parallel circuits exist over only parts of the length of the faulty line. Many successful applications of artificial neural networks (ANNs) to power systems have been demonstrated, including security assessment, load forecasting, control, etc. Recent applications in protection have covered fault diagnosis for electric power systems (Mohamed and Rao,1995), transformer protection (Zaman and Rahman, 1998) and generator protection (Megahed and Malik, 1999).However, almost all of these applications in protection merely use the ANN ability of classification, that is, ANNs only output 1 or 0.Various approaches have been published describing applications of ANNs to fault detection and location in transmission lines (Oleskovicz et al., 2001; Purushothama et al., 2001; Osowski and Salat, 2002).
In this project, a single-end fault detector and three fault locators are proposed for on-line applications using ANNs. A feedforward neural network based on the supervised backpropagation learning algorithm was used to implement the fault detector and locators. The neural fault detector and locators were trained and tested with a number of simulation cases by considering various fault conditions (fault types, fault locations, fault resistances and fault inception angles) and various power system data (source capacities, source voltages, source angles, time constants of the sources) in a selected network model.
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