Transmission Line Faults

Published: 2 December 2024| Version 1 | DOI: 10.17632/3dvjgvv5bz.1
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Description

This dataset contains simulated fault events for fault detection, classification, and localization in 110 kV power transmission lines. Three fault types were simulated: single-phase-to-ground fault (SPGF), two-phase short circuit (2PSC), and three-phase short circuit (3PSC). Each fault type was simulated with the fault location varying from the beginning to the end of the line in 5% increments, ensuring comprehensive coverage of fault positions along the transmission lines. A no-fault scenario was also included to represent normal operating conditions, where the system’s load was varied incrementally by 0.1 MW to simulate typical fluctuations in power demand. For each scenario, voltage and current data were extracted, including line-to-ground voltage magnitudes at all busbars and the initial short-circuit current magnitudes at the beginning of each transmission line. These features, totaling 24 input variables, are used to describe the state of the system during faults and normal operations. The output includes the fault type (SPGF, 2PSC, 3PSC, or no fault), fault line (Line1 to Line5, or None), and fault position along the line (0, 5, ..., 100). The dataset consists of 618 data points, representing various fault and normal scenarios along the transmission lines. A Python script was developed to automate the fault simulation, data extraction, and dataset generation process.

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Steps to reproduce

To reproduce the dataset, install DIgSILENT PowerFactory with a valid license. Set up a power transmission line model including components such as buses, generators, transmission lines, transformers, and loads, and define system parameters like line impedances and operating conditions. Develop a Python script to automate the simulation of faults in power transmission lines. The script should simulate various fault types (e.g., single-line-to-ground, double-line-to-ground, line-to-line, and three-phase faults) by configuring parameters like fault location, fault resistance, and fault duration. The script will collect key electrical parameters such as phase voltages and currents during the faults. The recorded data is then exported into a suitable format (e.g., CSV or Excel) for further analysis, with optional preprocessing to label fault types and locations for easy classification.

Categories

Power Engineering, Machine Learning, Power System Protection, Deep Learning

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