Lightning and Power Transmission Lines

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Term Paper

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Outline of Presentation

The Thunderstorm The Lightning Discharge (Stages) Lightning Currents and Related

Parameters Protection of Power-Transmission Effect of Lightning on Power

Transmission systems

The Thunderstorm

Global Distribution of Thunderstorms The Thundercloud Point-Discharge Currents The Lightning Discharge

Global Distribution of Thunderstorms

The Thundercloud

Two main classes: heat storms and frontal storms

Heat storm in tropics and mountain areas

Frontal storms in temperate regions Characterized by the electrical charge of

water droplets.

Point-Discharge Currents

Electrostatic field distribution about vertical lightning conductor (could induce lightning

discharge)

The Thundercloud continued

The Lightning Discharge (Stages)

Temporal Development of Flash to Ground

Mature Stage The Leader Stroke The Dart Leader

The Lightning Discharge (Stages) continued

Lightning Currents and Related Parameters Lightning Currents

Average lightning current amplitude is 25 kA (lowest 2kA, highest recorded 270 kA)

Frequency of Lightning Discharges (flash density) Varies from place to place Measured in thunderstorm days and

lightning flash density Lightning flash density for Greece = 3.7

(most recent data)

Protection of Power-Transmission Systems

Reasons for Protection Avoid power disruptions

Lightning protection methods The Air Terminal (overhead conductors) Tower Impedance (ability of tower to resist lightning) The Buried Earth System (underground grid of

conductors) Protective Leakage Paths-Pipe-Pipe Gaps (utilizes a gap

between conductors) Underground Cables (Utilizes insulation of the earth) Lightning Arresters (acts as over-voltage release valves)

Effect of Lightning on Power Transmission systems

Lightning LocationPrediction of Lightning Activities

Lightning Location

Use of magnetic link locators, lightning counters location within country-wide networks

Recorded by Meteorological Services and Power distribution companies over time

Empirical formula for calculation of lightning flash density correlate well with actual flash density obtain from magnetic counters (most recent data obtained from Greece and Japan)

Prediction of Lightning Activities

Prediction based on a variety of methods Modeling of lightning parameters Prediction based on neural networks Prediction based on fuzzy neural networks

All based on data obtained from lightning location from meteorological and power services.

Prediction based on Fuzzy Neural Networks

New technique used in Japan Gave better prediction of lighting strike

compared to neural networks Limitations: only predicts lightning strike few

hours before the strike.

Conclusions

Thunderstorms occur at highest at the equatorial belt and decreases towards the poles. Local thunderstorm activity varies from year to year.

Lightning data is available from the meteorological services of countries in the form of flash maps showing isokeraunic lines, i.e. lines joining areas have the same number of thunderstorm days.

The mechanism of light formation and discharge involves electrical charging of water droplets within a cloud leading to a dipole. The electrical field thus produces causes a cloud to cloud lightning discharge or a cloud to earth discharge.

The average lightning current of about 25 kA.

Conclusions continued

The earth flash density varies in different parts of the world with the earth flash density estimated at 1 to 2 flashes per 10 thunderstorm days.

A variety of protection mechanism exist for the lightning protection of transmission lines including ground conductors(air terminals), counterpoise , pipe-pipe gap, down-lead, grounding systems, underground cables and lightning arresters.

Conclusions continued

Traditional methods of lightning monitoring include the use of magnetic link direction finders and lightning counters.

Correlation between lightning data obtained from meteorological lightning networks and those obtained using standard formula show good agreement.

More modern methods have focused on lighting prediction using neural network and fuzzy neural network techniques, but these only provide prediction a couple of hours before the lightning strike.

End of Presentation

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