Lightning and Power Transmission Lines EE --- Term Paper By ---

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 Location  Prediction 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.

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