On the Statistical Characterization of Lightning-Induced Voltages

Andreotti, Amedeo; Mottola, Fabio; Pierno, Antonio; Proto, Daniela

doi:10.3390/app8040651

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Lightning‐induced voltages have a significant dependence not only on the distance but also on the relative position of the stroke location with respect to the line and to the observation point [1, 10, 11]. Even though the locations of the lightning strokes that induced the recorded voltages presented in this Section were not available, the shorter wavetails observed in the measured induced voltages, in comparison with the standard lightning impulse voltage, are coherent with calculations [1–11, 14, 15, 27] and also with measurements performed in other studies [16, 18–24, 27–31].…”

Section: Results and Analysissupporting

confidence: 73%

“…Frequency distributions of the peak values of lightning‐induced voltages have been proposed by Andreotti et al. based on computer simulations [14, 15]. Experimental studies including measurements of lightning surges on medium and low‐voltage lines have been performed in different countries [16–29].…”

Section: Introductionmentioning

confidence: 99%

“…Also essential is to understand how power equipment behave when subjected to lightning surges with non-standard wave shapes [12,13]. Frequency distributions of the peak values of lightning-induced voltages have been proposed by Andreotti et al based on computer simulations [14,15]. Experimental studies including measurements of lightning surges on medium and low-voltage lines have been performed in different countries [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of lightning‐induced voltages based on experimental data

Santos

Piantini

2020

High Voltage

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This paper presents an analysis of 64 lightning‐induced voltage waveforms recorded in an experimental system implemented in the campus of the University of São Paulo, Brazil. The voltages were measured on a 10 m high, 2.7 km long un‐energized overhead line consisting of two conductors installed on 6 m crossarms. One of the conductors had surge arresters, while the other one was left unprotected. Strictly speaking, all the recorded voltages on the unprotected conductor were bipolar, but in many cases only one semi‐cycle could be considered important for practical purposes. Different criteria are discussed for the determination of the waveshape parameters, which may differ significantly from those of the standard lightning impulse voltage (1.2/50 μs). Based on the proposed criterion for classifying the induced voltages, unipolar waveforms accounted for 67% of the total and, except for one case, all of them had positive polarity. The median values of the front time and time‐to‐half‐value were, respectively, 5.2 μs and 15.8 μs. Bipolar waveforms were 33% of the total and in approximately 62% of the cases the first semi‐cycle was of positive polarity. In 91% of the cases the voltage peak occurred either in the first or the second semi‐cycle.

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Section: Results and Analysissupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characteristics of lightning‐induced voltages based on experimental data

Santos

Piantini

2020

High Voltage

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“…Lightning is a common natural phenomenon mainly occurring within cloud-to-ground and cloud-to-cloud which, however, causes damages to electrical installations, buildings, and humans due to its large current as well as induced transient voltage [1][2][3]. Recently, affected by climate change and global warming, lightning has occurred more frequently compared to previous years.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network

et al. 2019

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Prediction of lightning occurrence has significant relevance for reducing potential damage to electric installations, buildings, and humans. However, the existing lightning warning system (LWS) operates using the threshold method and has low prediction accuracy. In this paper, an intelligent LWS based on an electromagnetic field and the artificial neural network was developed for improving lightning prediction accuracy. An electric field mill sensor and a pair of loop antennas were designed to detect the real-time electric field and the magnetic field induced by lightning, respectively. The change rate of electric field, temperature, and humidity acquired 2 min before lightning strikes, were used for developing the neural network using the back propagation algorithm. After observing and predicting lightning strikes over six months, it was verified that the proposed LWS had a prediction accuracy of 93.9%.

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“…By means of a Monte Carlo procedure applied to analytical formulations, Andreotti et al [20] focused on the statistical characterization of lightning-induced voltage peak values. The cases of lines with and without shield wires, above a perfectly conducting ground, were considered.…”

Section: Introductionmentioning

confidence: 99%

Análise de tensões induzidas por descargas atmosféricas medidas em uma linha experimental aérea casada

Santos¹

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I would like to thank the Universidade Federal do Amapá (Unifap) for the opportunity granted to qualify for a doctorate and to fully withdraw from the position of professor, as well as to all of the Electrical Engineering collegiate who supported me, especially Prof.Alaan Ubaiara and Prof. Kellen Gomes.I thank my family for always being present in my life, my parents, Pedro Paulo and Rosemeire, for the education given to me, and immense gratitude to Aline Thiele who knew how to be a great companion when I could not be there and who gave me the strength to stay focused. I would like to thank my great friend Hellen Paiva for her help in developing the LabVIEW program, which was used as a tool for the development of this Ph.D. Dissertation, without her contribution it would not have the same level of quality. I would like to thank the CENDAT Group, Engineers Acácio Neto and Milton Shigihara who welcomed me on my admission, explaining the experimental system, clearing up my doubts whenever they arose. And Prof. Dr. Arnaldo G. Kanashiro for encouraging my studies, and I also thank Diana for everything I learned with her help and friendship. Finally, I would like to thank the Institute of Energy and the Environment of the University of São Paulo, which provided the conditions for the development of this work, the Financier of Studies and Projects (FINEP), Companhia Paulista de Força e Luz (CPFL) and Rio Grande Energia (RGE), who provided financial support for the development of the two experimental systems (USP/SP System: CPFL Support and URI system: RGE, CPFL and FINEP support)."If you don't know, the thing to do is not to get scared, but to learn."

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On the Statistical Characterization of Lightning-Induced Voltages

Cited by 13 publications

References 21 publications

Characteristics of lightning‐induced voltages based on experimental data

Characteristics of lightning‐induced voltages based on experimental data

An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network

Análise de tensões induzidas por descargas atmosféricas medidas em uma linha experimental aérea casada

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