Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2
datasets available to search
ShareScore release 0.7.1
Dataset results
2 results for “flashover”
Dataset of lightning flashovers on medium voltage distribution lines
<p>This <strong>synthetic dataset</strong> was generated from <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines. The dataset is <strong>hierarchical</strong> in nature (see below for more information) and <strong>class imbalanced</strong>.</p> <p>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. Three different methods of indirect strike analysis have been implemented, as follows: Rusck's model, Chowdhuri-Gross model and Liew-Mar model. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of two independent distribution lines, with heights of 12 m and 15 m, each with a flat configuration of phase conductors. Twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart [2]. CFO level of the 12 m distribution line is 150 kV and that of the 15 m distribution line is 160 kV. Dataset consists of <strong>10,000 simulations</strong> for each of the distribution lines.</p> <p>Dataset contains following variables (features):</p> <ul> <li>'<em>dist</em>': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'<em>ampl</em>': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'<em>front</em>': lightning current wave-front time (us), generated from the Log-Normal distribution; it needs to be emphasized that amplitudes (ampl) and wave-front times (front), as random variables, have been generated from the appropriate bivariate probability distribution which includes statistical correlation between these variates,</li> <li>'<em>veloc</em>': velocity of the lightning return-stroke current defined indirectly through the parameter "w" that is generated from the Uniform distribution [50, 500] m/us, which is then used for computing the velocity from the following relation: v = c/sqrt(1+w/I), where "c" is the speed of light in free space (300 m/us) and "I" is the lightning-current amplitude,</li> <li>'<em>shield</em>': 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,</li> <li>'<em>Ri</em>': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'<em>EGM</em>': 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],</li> <li>'<em>ind</em>': indirect stroke model used for analyzing near-by indirect lightning strikes; following options were implemented: 'rusk' for the Rusck's model, 'chow' for the Chowdhuri-Gross model (with Jakubowski modification) and 'liew' for the Liew-Mar model; statistical distribution of these three models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.6, 0.2, 0.2],</li> <li>'<em>CFO</em>': critical flashover voltage level of the distribution line's insulation (kV),</li> <li>'<em>height</em>': height of the phase conductors of the distribution line (m),</li> <li>'<em>flash</em>': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome/label (i.e. binary class).</li> </ul> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references cited below.</p> <p><strong>References</strong>:</p> <ol> <li>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</li> <li>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.</li> <li>A. Borghetti, C. A. Nucci and M. Paolone, An Improved Procedure for the Assessment of Overhead Line Indirect Lightning Performance and Its Comparison with the IEEE Std. 1410 Method, IEEE Transactions on Power Delivery, Vol. 22, No. 1, 2007, pp. 684-692.</li> </ol>
Lightning flashover simulations on medium voltage distribution lines
<p>[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information).</p> <p>Dataset has been generated from the <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> 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.</p> <p>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.</p> <p><strong>Dataset</strong> consists of the following variables:</p> <ul> <li>'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,</li> <li>'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,</li> <li>'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) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'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],</li> <li>'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,</li> <li>'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 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,</li> <li>'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).</li> </ul> <p><strong>Note</strong>: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). 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) and CFO levels.</p> <p><strong>Important</strong>: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved.</p> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.</p> <p><strong>References</strong>:</p> <p>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.</p> <p>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.