A Probabilistic Estimation for Dynamic Thermal Rating of Transmission Lines

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Probabilistic Estimation for Dynamic Thermal Rating of Transmission Lines

Popis výsledku v původním jazyce

Dynamic Thermal Line Rating (DTLR) provides actual current-carrying capacity of transmission lines by considering weather conditions perceived to be influential on line thermal capacity. These weather variables include ambient temperature, wind speed, and wind direction. In this paper, a probability technique is adopted to effectively model the existing uncertainties in weather variables. A probability distribution is assigned to each variable to account for the inherent uncertainties in weather data. Monte Carlo Simulation (MCS) technique is then employed to generate different scenarios for relevant weather data. Output of MCS is fed to the IEEE model to obtain the probability distribution of sectional line ampacity estimated at each line span. Next, probability distribution of line ampacity will be calculated as the minimum of the ampacities estimated at each line span. Expected value and related percentiles of line ampacity can be derived from its probability distribution. The proposed approach would enable system operators to make decisions on DTLR accounting for risk and degree of uncertainty that power utilities are willing to accept.

Název v anglickém jazyce

A Probabilistic Estimation for Dynamic Thermal Rating of Transmission Lines

Popis výsledku anglicky

Dynamic Thermal Line Rating (DTLR) provides actual current-carrying capacity of transmission lines by considering weather conditions perceived to be influential on line thermal capacity. These weather variables include ambient temperature, wind speed, and wind direction. In this paper, a probability technique is adopted to effectively model the existing uncertainties in weather variables. A probability distribution is assigned to each variable to account for the inherent uncertainties in weather data. Monte Carlo Simulation (MCS) technique is then employed to generate different scenarios for relevant weather data. Output of MCS is fed to the IEEE model to obtain the probability distribution of sectional line ampacity estimated at each line span. Next, probability distribution of line ampacity will be calculated as the minimum of the ampacities estimated at each line span. Expected value and related percentiles of line ampacity can be derived from its probability distribution. The proposed approach would enable system operators to make decisions on DTLR accounting for risk and degree of uncertainty that power utilities are willing to accept.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) : proceedings

ISBN

978-1-5090-2320-2

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Florencie

Datum konání akce

7. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000387085800420