Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming

The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically an...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Yan, Guoan, Li, Zhengshuo
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Systems and Control
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	Yan, Guoan Li, Zhengshuo
description	The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF model. Two acceleration solution strategies are further suggested to reduce the computational time. Case studies demonstrate the superior accuracy and comparable computational time of the proposed method when compared to three common approaches.
doi_str_mv	10.48550/arxiv.2312.15831
format	Article
fullrecord	<record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2312_15831</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2312_15831</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-293c93b6fd5ae3d47427bb48ef11126ef22575d679f562beb11f31783b26ef453</originalsourceid><addsrcrecordid>eNotz7FOwzAYBGAvDKjwAEz4BRz623GcjChQqJSqHbqiyK5_R5YSG7lJWt4eWjrdcKeTPkKeYJnlpZTLF53Ofs64AJ6BLAXck6_tNPYeE_PDMAWkb3rUzCY_Y6CND6gT3cUTJrrq44luosWe1jEcxzQdRh8Dnb2mG39Gy9ZhxO5vuUuxS3oYfOgeyJ3T_REfb7kg-9X7vv5kzfZjXb82TBcKGK_EoRKmcFZqFDZXOVfG5CU6AOAFOs6lkrZQlZMFN2gAnABVCnMpcykW5Pn_9uprv5MfdPppL8726hS_zdpOIA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming</title><source>arXiv.org</source><creator>Yan, Guoan ; Li, Zhengshuo</creator><creatorcontrib>Yan, Guoan ; Li, Zhengshuo</creatorcontrib><description>The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF model. Two acceleration solution strategies are further suggested to reduce the computational time. Case studies demonstrate the superior accuracy and comparable computational time of the proposed method when compared to three common approaches.</description><identifier>DOI: 10.48550/arxiv.2312.15831</identifier><language>eng</language><subject>Computer Science - Systems and Control</subject><creationdate>2023-12</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2312.15831$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2312.15831$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan, Guoan</creatorcontrib><creatorcontrib>Li, Zhengshuo</creatorcontrib><title>Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming</title><description>The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF model. Two acceleration solution strategies are further suggested to reduce the computational time. Case studies demonstrate the superior accuracy and comparable computational time of the proposed method when compared to three common approaches.</description><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7FOwzAYBGAvDKjwAEz4BRz623GcjChQqJSqHbqiyK5_R5YSG7lJWt4eWjrdcKeTPkKeYJnlpZTLF53Ofs64AJ6BLAXck6_tNPYeE_PDMAWkb3rUzCY_Y6CND6gT3cUTJrrq44luosWe1jEcxzQdRh8Dnb2mG39Gy9ZhxO5vuUuxS3oYfOgeyJ3T_REfb7kg-9X7vv5kzfZjXb82TBcKGK_EoRKmcFZqFDZXOVfG5CU6AOAFOs6lkrZQlZMFN2gAnABVCnMpcykW5Pn_9uprv5MfdPppL8726hS_zdpOIA</recordid><startdate>20231225</startdate><enddate>20231225</enddate><creator>Yan, Guoan</creator><creator>Li, Zhengshuo</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20231225</creationdate><title>Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming</title><author>Yan, Guoan ; Li, Zhengshuo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-293c93b6fd5ae3d47427bb48ef11126ef22575d679f562beb11f31783b26ef453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Yan, Guoan</creatorcontrib><creatorcontrib>Li, Zhengshuo</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yan, Guoan</au><au>Li, Zhengshuo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming</atitle><date>2023-12-25</date><risdate>2023</risdate><abstract>The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF model. Two acceleration solution strategies are further suggested to reduce the computational time. Case studies demonstrate the superior accuracy and comparable computational time of the proposed method when compared to three common approaches.</abstract><doi>10.48550/arxiv.2312.15831</doi><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	DOI: 10.48550/arxiv.2312.15831
ispartof
issn
language	eng
recordid	cdi_arxiv_primary_2312_15831
source	arXiv.org
subjects	Computer Science - Systems and Control
title	Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T16%3A15%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Outlier-immune%20Data-driven%20Linear%20Power%20Flow%20Model%20Construction%20via%20Mixed-Integer%20Programming&rft.au=Yan,%20Guoan&rft.date=2023-12-25&rft_id=info:doi/10.48550/arxiv.2312.15831&rft_dat=%3Carxiv_GOX%3E2312_15831%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true