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Published March 24, 2022 | Version 1.0
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Lightning flashover simulations on medium voltage distribution lines

Creators

  • 1. University of Split, FESB

Description

Dataset has been generated from the Monte Carlo simulations of lightning flashovers on medium voltage (MV) distribution lines. It is suitable for training machine learning models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.

Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.

Dataset consists of the following variables:

  • 'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,
  • 'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),
  • 'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,
  • 'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,
  • 'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side),
  • 'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],
  • 'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,
  • 'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).

Note: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV, and that the diameters of the phase conductors and shield wires are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm. Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry).

Important: Use the latest version of the dataset!

Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.

References:

J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.

A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.

Files

distlines.csv

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