Ranking transmission lines by overload probability using the empirical rate function

Brendan Patch; Bert Zwart

doi:10.1109/PMAPS47429.2020.9183567

Ranking transmission lines by overload probability using the empirical rate function

Brendan Patch, Bert Zwart

Stochastic Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.

Original language	English
Title of host publication	2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings
Publisher	IEEE/LEOS
Number of pages	6
ISBN (Electronic)	9781728128221
ISBN (Print)	978-1-7281-2823-8
DOIs	https://doi.org/10.1109/PMAPS47429.2020.9183567
Publication status	Published - 21 Aug 2020
Event	2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) - Liege, Belgium Duration: 18 Aug 2020 → 21 Aug 2020

Conference

Conference	2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Period	18/08/20 → 21/08/20

Keywords

Benchmark testing
Power transmission lines
Gaussian distribution
Transmission line matrix methods
Standards
Maximum likelihood estimation
Energy systems
ranking
large deviations theory
non-parametric

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PMAPS47429.2020.9183567

Cite this

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title = "Ranking transmission lines by overload probability using the empirical rate function",

abstract = "We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.",

keywords = "Benchmark testing, Power transmission lines, Gaussian distribution, Transmission line matrix methods, Standards, Maximum likelihood estimation, Energy systems, ranking, large deviations theory, non-parametric",

author = "Brendan Patch and Bert Zwart",

year = "2020",

month = aug,

day = "21",

doi = "10.1109/PMAPS47429.2020.9183567",

language = "English",

isbn = "978-1-7281-2823-8",

booktitle = "2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings",

publisher = "IEEE/LEOS",

note = "2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) ; Conference date: 18-08-2020 Through 21-08-2020",

}

Patch, B & Zwart, B 2020, Ranking transmission lines by overload probability using the empirical rate function. in 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings., 9183567, IEEE/LEOS, 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18/08/20. https://doi.org/10.1109/PMAPS47429.2020.9183567

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AU - Patch, Brendan

AU - Zwart, Bert

PY - 2020/8/21

Y1 - 2020/8/21

N2 - We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.

AB - We develop a non-parametric procedure for ranking transmission lines in a power system according to the probability that they will overload due to stochastic renewable generation or demand-side load fluctuations, and compare this procedure to several benchmark approaches. Using the IEEE 39-bus test network we provide evidence that our approach, which statistically estimates the rate function for each line, is highly promising relative to alternative methods which count overload events or use incorrect parametric assumptions.

KW - Benchmark testing

KW - Power transmission lines

KW - Gaussian distribution

KW - Transmission line matrix methods

KW - Standards

KW - Maximum likelihood estimation

KW - Energy systems

KW - ranking

KW - large deviations theory

KW - non-parametric

UR - https://www.scopus.com/pages/publications/85091339394

U2 - 10.1109/PMAPS47429.2020.9183567

DO - 10.1109/PMAPS47429.2020.9183567

M3 - Conference contribution

SN - 978-1-7281-2823-8

BT - 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings

PB - IEEE/LEOS

T2 - 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

Y2 - 18 August 2020 through 21 August 2020

ER -

Ranking transmission lines by overload probability using the empirical rate function

Abstract

Conference

Keywords

UN SDGs

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