Abstract
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Highlights
Distribution Systems use several intelligent electronic devices (IEDs) such as digital protection devices, modern metering devices and Remote Terminal Units
Each LCC block represents a transmission line setting in ALTERNATE TRANSIENT PROGRAM (ATP)-DRAW
Evolutionary Optimization Algorithms can be successfully implemented for accurate fault diagnosis in a double circuit high-voltage transmission line (HVTL)
Summary
Distribution Systems use several intelligent electronic devices (IEDs) such as digital protection devices, modern metering devices and Remote Terminal Units. These devices are generally equipped with super-processors, which are capable of advanced computations within a shorter time scale. With such advanced metrics and computation, faster and improved fault detection and identification techniques can be investigated, for a possible implementation in real time scenario. The locating process is time consuming and the system becomes bulky Due to these complications, a number of automated fault location methods have been introduced for the process of fault location. The fault methods for distribution networks are categorized as Impedance based
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