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Predicting lightning-related outages in power distribution systems : a statistical approach

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Chowdhury, B., & Doostan, M. (2020). Predicting lightning-related outages in power distribution systems : a statistical approach. Ieee Access. https://doi.org/doi:10.1109/ACCESS.2020.2991923
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Description

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.

Details
Title

Predicting lightning-related outages in power distribution systems : a statistical approach

Author(s)

Chowdhury, Badrul
Doostan, Milad

UNC Charlotte Affiliate Type

Faculty

Publisher

IEEE

Extent

1 online resource

Date Published

2020-05-06

Material Type

Journal article

Genre

articles

Resource Type

text

Abstract

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.

Subjects--Topics

Machine learning
Electric power distribution--Communication systems
Lightning

Is Part Of

IEEE Access

Volume

8

ISSN

2169-3536

Beginning Page Number

84541

Ending Page Number

84550

Language

English

Published Version (Please cite this version)

doi:10.1109/ACCESS.2020.2991923

Publisher's Version

https://doi.org/10.1109/ACCESS.2020.2991923

Permalink

http://hdl.handle.net/20.500.13093/work:108

Rights

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