Cloud-to-ground lightning warnings based on electric field-mill measurements in Brazil

Ferro, Marco; Yamasaki, Jorge; Pimentel, D. R. M.; Naccarato, Kleber P.; Saba, Marcelo M. F.

doi:10.1109/iclp.2010.7845808

Cited by 2 publications

(2 citation statements)

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“…In addition, the radar echoes have a high temporal‐spatial resolution and warning range but lack specific lightning flash information [19]. Using the atmospheric electric fields is a relatively simple and easy means but has a short warning time and range [18]. In order to further improve the accuracy of lightning early warning, some scholars have also adopted some data mining methods, especially the more popular BP neural network method.…”

Section: Risk Early Warningmentioning

confidence: 99%

“…In recently years, some early warning methods of lightning risk for transmission lines have been proposed and are mainly based on the data of lightning location system [15][16][17]. In fact, multiple information such as atmospheric electric fields, satellite images, and radar echoes can also be used to predict lightning activity [18,19]. To integrating different characteristics of each parameter to achieve precise lightning early warning needs to be further strengthened.…”

Section: Introductionmentioning

confidence: 99%

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Study on lightning risk assessment and early warning for UHV DC transmission channel

Wang

et al. 2019

High Voltage

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Operation data show lightning faults account for >70% for the main ultra-high voltage (UHV) DC transmission channels, very different from the design view. In order to accurately master the lightning characteristics and the lightning protection performance of the line so as to propose solutions to weak points, this study firstly obtains and analyses the density and strength distributions of lightning risk source. Then the study proposes a set of risk assessment process where the key model electrogeometric model is improved according to the polarity effect of DC line. Then the work realises the calculation of the lightning shielding failure risk of single tower and whole line. The example shows the assessment result is consistent to the line's actual operation. Next, to further evaluate and predict the lightning risk in real time, the study adopts the backpropagation neural network algorithm to integrate the lightning detection data, atmospheric electric fields, and radar echoes to develop the early warning model of lightning risk source, and proposed a method to realise the early warning of lightning damage risk for UHV DC channels. The results show that the effective warning ratio is 73% and the failure-to-warn ratio is 27% which indicates very good application effects.

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Section: Risk Early Warningmentioning

confidence: 99%