An Efficient Methodology for the Evaluation of the Lightning Performance of Overhead Lines

Mestriner, Daniele; Brignone, Massimo; Procopio, Renato; Nicora, Martino; Fiori, Elisabetta; Piantini, Alexandre; Rachidi, Farhad

doi:10.1109/temc.2021.3054427

Cited by 12 publications

(2 citation statements)

References 24 publications

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“…Indeed, the exact analytical dependence of the lightning-induced voltage on the distance between the point of impact and the line is difficult to evaluate since it requires to compute lightning electromagnetic fields and then the solution of the Maxwell's equations describing the field-to-line coupling problem. However, simplified approaches have verified that, at least, the induced voltage peak can be approximated with an expression that is linear [23] or quadratic [35] with respect to the inverse of such a distance. Hence, near the line, small y variations can produce significant variations in the induced voltage, and this may affect the capability of the ML model to regress the y coordinate.…”

Section: B Case 2: Variable Peak Current and Non-pec Groundmentioning

confidence: 99%

Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines With a Machine Learning Approach

Nicora,

Tucci,

Barmada

et al. 2024

IEEE Trans. Electromagn. Compat.

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In this article, a machine-learning-based model for the regression of cloud-to-ground lightning location and peak current from time-domain waveforms of lightning-induced voltage measurements on overhead transmission lines is presented. A principal component analysis (PCA) procedure is applied for extracting significant features and decreasing the dimension of the input vector. Then, a shallow neural network is trained with the results of the PCA. The obtained results show that the proposed approach can be the base for a tool able to regress lighting location with an accuracy comparable to or even better than traditional methods [i.e., lightning location system (LLS)] and provide a peak current estimate more accurate than LLS and more actual and widespread than direct tower measurements (which are limited to a reduced number of recorded events in some specific regions). Such a tool would also have significant advantages in terms of costs, since it would not require a dedicated instrumentation.

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Section: B Case 2: Variable Peak Current and Non-pec Groundmentioning

confidence: 99%

Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines With a Machine Learning Approach

Nicora,

Tucci,

Barmada

et al. 2024

IEEE Trans. Electromagn. Compat.

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“…Regardless of the protection measure used, a study of the effect of a lightning strike must always be performed in which, through modeling and simulation, the effectiveness of the protection scheme is evaluated to verify that the electrical stresses applied to the equipment during lightning events are maintained within their support margins [30,31]. For these studies, adequate modeling of all parts and components, such as line insulators, poles, grounding, transformers, and protection devices, among others, is required, since the reliability of the results depends directly on the quality and accuracy of the models used [24,32].…”

Section: Introductionmentioning

confidence: 99%

External Multi-Gap Lightning Arrester Modeling Using the Integration Method

Rodríguez-Serna,

Villa-Acevedo,

López-Lezama

2024

Energies

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Electric power distribution networks are exposed to both internal and external disturbances. Lightning strikes are among the latter and are responsible for a significant percentage of damage in distribution transformers, especially in rural areas. Electric utilities must pay special attention to prevent damage and service interruption due to these unforeseeable events. In this context, Surge Protection Devices (SPDs) combined with a series of external air gaps are designed to safeguard electric equipment and systems from transient over-voltages. There are several well-known models of SPDs in the specialized literature; nonetheless, few studies have been carried out with external gaps and multi-gaps. The main contribution of this paper is a methodology to model the disruptive effect in an external air gap by determining the parameters of Kind’s and Chowdhuri’s models using the integration method. The adjustment of the model parameters is carried out by a genetic algorithm (GA). The proposed model was tested and validated using experimental measurements, and its capability to predict the time-to-breakdown under different impulse voltages was verified.

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Flashover pattern analysis for 275 kV double circuit transmission lines during direct lightning strikes

Ahmed,

Illias,

Mokhlis

et al. 2024

Electric Power Systems Research

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An Efficient Methodology for the Evaluation of the Lightning Performance of Overhead Lines

Cited by 12 publications

References 24 publications

Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines With a Machine Learning Approach

Lightning Location and Peak Current Estimation From Lightning-Induced Voltages on Transmission Lines With a Machine Learning Approach

External Multi-Gap Lightning Arrester Modeling Using the Integration Method

Flashover pattern analysis for 275 kV double circuit transmission lines during direct lightning strikes

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