Experimental Investigation to Calculate the Lightning Outage Rate of a Distribution System

Miyazaki, Teru; Okabe, Shigemitsu

doi:10.1109/tpwrd.2010.2053563

Cited by 24 publications

(11 citation statements)

References 12 publications

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“…[6], a model of calculating the frequency of lightning strike to the power distribution lines per unit area (1 km 2 ) considering the density of power distribution lines and the structures around the power distribution lines is proposed. In this model, the direct lightning strike rate is a function of the installation rate of pole transformers and the density of power distribution lines.…”

Section: E Calculation Of Factor In Each Regionmentioning

confidence: 99%

Study on lightning hazard evaluation for power distribution lines

Ishimoto

Asakawa

2013

2013 International Symposium on Lightning Protection (XII SIPDA)

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For a rational lightning protection design of power distribution lines, it is important to consider the cost performance of countermeasures. For this purpose, it is necessary to evaluate the lightning hazard of power distribution lines quantitatively, considering the influences of lightning characteristics, the density of the power distribution lines and the structures around the power distribution lines. In this paper, first, we describe the calculation results of lightning outage rates of power distribution line. Next, we propose a lightning hazard evaluation method considering the characteristics of each region. By applying this method with actual equipment data, we can evaluate the quantitative lightning hazard of each region.

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Section: E Calculation Of Factor In Each Regionmentioning

confidence: 99%

Study on lightning hazard evaluation for power distribution lines

Ishimoto

Asakawa

2013

2013 International Symposium on Lightning Protection (XII SIPDA)

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“…The impedance of the return-stroke channel is assumed 1 kn [17]. The variation of the channel impedance from 1 kn to 400 n [4,6], often used in the simulation of the direct strokes to the medium-voltage line, results in little difference of the estimated flashover rate.…”

Section: Return-stroke Current Parametersmentioning

confidence: 99%

“…The proportion of faults caused by lightning, however, tends to increase with often reaching beyond 30 %. The lightning overvoltage on the distribution line, associated with direct hits, is looked upon as the main factor in its insulation design [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Flashover rate of medium-voltage line estimated with lightning parameters in Japan

Michishita

Sakai

Yokoyama

et al. 2012

2012 International Conference on Lightning Protection (ICLP)

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For the effective insulation design of a medium voltage line, it is im portant to estimate flashover rate accurately. In this paper, flashover rate due to direct strokes to the line is evaluated by using peak currents obtained in southern Kyushu by the JLDN. In the evaluation, the correlation between the front duration and the peak current is taken into account as well as the low-voltage line. As the result, the estimated flashover rate for the flash is in reasonable agreement with the observed rate.

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“…Lightning is a major cause of outages in power distribution systems [1]. Transient over-voltages caused by direct or indirect lightning strikes may inflict severe damages to the susceptible equipment and can produce detrimental effects on power quality and reliability of the system.…”

Section: Introductionmentioning

confidence: 99%

Predicting Lightning-Related Outages in Power Distribution Systems: A Statistical Approach

Doostan

Chowdhury

2020

IEEE Access

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This paper presents a novel data-driven approach for predicting lightning-related outages that occur in power distribution systems on a daily basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to adequately identify characteristics of lightning-related outages and to explore the relationship between these outages and weather-related variables (thunderstorm events). In this paper, these outages are clustered into a few manageable groups. Then, a probabilistic model is presented to estimate the likelihood of each group of outages. Finally, a machine learning classification algorithm that can handle the imbalanced problem is developed to predict what group will the outage belong to on a specific day in a specific area of the system under study. Actual outage data, obtained from a major utility in the U.S., in addition to radar weather forecast data are utilized to build the proposed approach. Also, three case studies are provided to show several issues associated with predicting lightning-related outages, and to demonstrate how the proposed approach can address those problems adequately.

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Experimental Investigation to Calculate the Lightning Outage Rate of a Distribution System

Cited by 24 publications

References 12 publications

Study on lightning hazard evaluation for power distribution lines

Study on lightning hazard evaluation for power distribution lines

Flashover rate of medium-voltage line estimated with lightning parameters in Japan

Predicting Lightning-Related Outages in Power Distribution Systems: A Statistical Approach

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