Lightning insulation coordination is based on statistical approaches. This allows to correlate the electrical stress caused by lightning and the electrical strength of the insulations, both having probabilistic nature. This chapter provides an example of lightning insulation coordination. Specifically, it deals with the statistical appraisal of the so-called lightning performance of distribution systems, carried out by means of Monte Carlo (MC) simulations. The relevant application to both the cases of direct and indirect lightning events, considering the correlation between the probability distributions of the lightning current parameters, is described and discussed. In particular, the application to the indirect events is based on the definition of a surface around the power line and on the calculation of the induced voltages along the line caused by indirect events having stroke location uniformly distributed within such a surface. The result obtained through the MC simulations is fmally scaled taking into account the annual number of fl ashes per square kilometer expected in the region of interest. In order to obtain significant results, two aspects need to be considered: the surface around the power line should be large enough in order to collect all the events that may endanger the insulation, and the density of the stroke locations should be sufficiently high. Therefore, for medium voltage systems, or even more for the case of low voltage ones, the area can reach a large value indeed, and the number of events to be considered can be consequently huge. The chapter also describes the application of the stratified sampling technique able to reduce the computation effort typical to this type of calculation.

Borghetti A., Napolitano F., Nucci C.A., Tossani F. (2020). Application of the Monte Carlo method to lightning protection and insulation coordination practices. Londra : Institution of Engineering and Technology [10.1049/PBPO172G_ch1].

### Application of the Monte Carlo method to lightning protection and insulation coordination practices

#### Abstract

Lightning insulation coordination is based on statistical approaches. This allows to correlate the electrical stress caused by lightning and the electrical strength of the insulations, both having probabilistic nature. This chapter provides an example of lightning insulation coordination. Specifically, it deals with the statistical appraisal of the so-called lightning performance of distribution systems, carried out by means of Monte Carlo (MC) simulations. The relevant application to both the cases of direct and indirect lightning events, considering the correlation between the probability distributions of the lightning current parameters, is described and discussed. In particular, the application to the indirect events is based on the definition of a surface around the power line and on the calculation of the induced voltages along the line caused by indirect events having stroke location uniformly distributed within such a surface. The result obtained through the MC simulations is fmally scaled taking into account the annual number of fl ashes per square kilometer expected in the region of interest. In order to obtain significant results, two aspects need to be considered: the surface around the power line should be large enough in order to collect all the events that may endanger the insulation, and the density of the stroke locations should be sufficiently high. Therefore, for medium voltage systems, or even more for the case of low voltage ones, the area can reach a large value indeed, and the number of events to be considered can be consequently huge. The chapter also describes the application of the stratified sampling technique able to reduce the computation effort typical to this type of calculation.
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2020
Lightning Interaction with Power Systems
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Borghetti A., Napolitano F., Nucci C.A., Tossani F. (2020). Application of the Monte Carlo method to lightning protection and insulation coordination practices. Londra : Institution of Engineering and Technology [10.1049/PBPO172G_ch1].
Borghetti A.; Napolitano F.; Nucci C.A.; Tossani F.
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/11585/857211`
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