Machine Learning for Inferring Phase Connectivity in Distribution Networks

被引：0

作者：

Bandyopadhyay, Sambaran ^{[1
]}

Kota, Ramachandra ^{[1
]}

Mitra, Rajendu ^{[1
]}

Arya, Vijay ^{[1
]}

Sullivan, Brian ^{[2
]}

Mueller, Richard ^{[2
]}

Storey, Heather ^{[2
]}

Labut, Gerard ^{[2
]}

机构：

[1] IBM Res, Yorktown Hts, NY 10598 USA

[2] DTE Energy, Detroit, MI USA

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM) | 2015年

关键词：

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work focuses on inferring customer to phase connectivity using machine learning techniques. Using voltage time series measurements collected from customer smart meters as the feature set for training classifiers, we study the performance of supervised, semi-supervised and unsupervised techniques. We report analysis and field validation results based on real smart meter measurements collected from three feeder circuits of a large distribution network in North America.

引用

页码：91 / 96

页数：6

共 50 条

[1] Inferring gene regulatory networks by machine learning methods
Supper, Jochen
Frohlich, Holger
Spieth, Christian
Drager, Andreas
Zell, Andreas
[J]. PROCEEDINGS OF THE 5TH ASIA- PACIFIC BIOINFOMATICS CONFERENCE 2007, 2007, 5 : 247 - +
[2] Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system
Casert, C.
Vieijra, T.
Nys, J.
Ryckebusch, J.
[J]. PHYSICAL REVIEW E, 2019, 99 (02)
[3] Inferring connectivity of interacting phase oscillators
Yu, Dongchuan
Fortuna, Luigi
Liu, Fang
[J]. CHAOS, 2008, 18 (04)
[4] Application of machine learning in water distribution networks
New University of Lisbon and Uninova, Faculty of Sciences and Technology Quinta da Torre, 2825 Monte Caparica, Portugal
[J]. Intelligent Data Analysis, 1998, 2 (04): : 311 - 332
[5] On Inferring Functional Connectivity with Directed Information in Neuronal Networks
Cai, Zhiting
Neveu, Curtis L.
Byrne, John H.
Aazhang, Behnaam
[J]. 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, 2016, : 356 - 360
[6] A mathematical framework for inferring connectivity in probabilistic neuronal networks
Nykamp, Duane Q.
[J]. MATHEMATICAL BIOSCIENCES, 2007, 205 (02) : 204 - 251
[7] Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning
Bernardini, Giulia
van Iersel, Leo
Julien, Esther
Stougie, Leen
[J]. MOLECULAR PHYLOGENETICS AND EVOLUTION, 2024, 199
[8] An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Browne, Fiona
Wang, Haiying
Zheng, Huiru
Azuaje, Francisco
[J]. JOURNAL OF INTEGRATIVE BIOINFORMATICS, 2006, 3 (02) : 230 - 246
[9] Learning, connectivity and networks
Haythornthwaite, Caroline
[J]. INFORMATION AND LEARNING SCIENCES, 2019, 120 (1-2) : 19 - 38
[10] Applications of Machine Learning in Quantum Key Distribution Networks
Zhao, Yongli
Zhang, Kaixin
Zhu, Qingcheng
Wang, Hua
Yu, Xiaosong
Zhang, Jie
[J]. 2021 OPTOELECTRONICS GLOBAL CONFERENCE (OGC 2021), 2021, : 227 - 229

← 1 2 3 4 5 →