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On Practical Implementation of Electromagnetic Models of Lightning Return-Strokes
Open AccessArticle

A Methodology to Reduce the Computational Effort in the Evaluation of the Lightning Performance of Distribution Networks

1
Department of Mechanical, Energy, Management and Transportation Engineering, University of Genoa, I-16145 Genoa, Italy
2
Department of Naval, Electrical, and ICT Engineering, University of Genoa, I-16145 Genoa, Italy
3
EMC Laboratory, Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Vernon Cooray
Atmosphere 2016, 7(11), 147; https://doi.org/10.3390/atmos7110147
Received: 24 August 2016 / Revised: 7 November 2016 / Accepted: 15 November 2016 / Published: 20 November 2016
The estimation of the lightning performance of a power distribution network is of great importance to design its protection system against lightning. An accurate evaluation of the number of lightning events that can create dangerous overvoltages requires a huge computational effort, as it implies the adoption of a Monte Carlo procedure. Such a procedure consists of generating many different random lightning events and calculating the corresponding overvoltages. The paper proposes a methodology to deal with the problem in two computationally efficient ways: (i) finding out the minimum number of Monte Carlo runs that lead to reliable results; and (ii) setting up a procedure that bypasses the lightning field-to-line coupling problem for each Monte Carlo run. The proposed approach is shown to provide results consistent with existing approaches while exhibiting superior Central Processing Unit (CPU) time performances. View Full-Text
Keywords: lightning performance; Monte Carlo method; Critical Flashover Voltage lightning performance; Monte Carlo method; Critical Flashover Voltage
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MDPI and ACS Style

Bendato, I.; Brignone, M.; Delfino, F.; Procopio, R.; Rachidi, F. A Methodology to Reduce the Computational Effort in the Evaluation of the Lightning Performance of Distribution Networks. Atmosphere 2016, 7, 147.

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