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Voltage Correlations in Smart Meter Data

Published: 10 August 2015 Publication History

Abstract

The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field.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 shows that voltage time series measurements collected from customer smart meters exhibit correlations that are consistent with the hierarchical structure of the distribution network. These correlations may be leveraged to cluster customers based on common ancestry and help verify and correct an existing connectivity model. Additionally, customers may be clustered in combination with voltage data from circuit metering points, spatial data from the geographical information system, and any existing but partially accurate connectivity model to infer customer to transformer and phase connectivity relationships with high accuracy.
We report analysis and validation results based on data collected from multiple feeders of a large electric distribution network in North America. To the best of our knowledge, this is the first large scale measurement study of customer voltage data and its use in inferring network connectivity information.

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References

[1]
V. Arya, T. S. Jayram, S. Pal, and S. Kalyanaraman. Inferring connectivity model from meter measurements in distribution networks. In e-Energy, pages 173--182, 2013.
[2]
V. Arya and R. Mitra. Voltage-based clustering to identify connectivity relationships in distribution networks. In SmartGridComm, pages 7--12, 2013.
[3]
V. Arya, R. Mitra, R. Mueller, H. Storey, G. Labut, J. Esser, and B. Sullivan. Voltage analytics to infer customer phase. In IEEE PES Innovative Smart Grid Technologies(ISGT) Europe, 2014.
[4]
J. Bouford and C. Warren. Many states of distribution. Power and Energy Magazine, IEEE, 5(4):24--32, 2007.
[5]
K. Caird. Meter Phase Identification. US Patent Application 20100164473, January 2010. Patent No. 12/345702.
[6]
G. Clark. A changing map: Four decades of service restoration at alabama power. Power and Energy Magazine, IEEE, 12(1):64--69, Jan 2014.
[7]
M. Dilek. Integrated Design of Electrical Distribution Systems: Phase Balancing and Phase Prediction Case Studies. PhD thesis, Virginia Polytechnic Institute and State University, 2001.
[8]
M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA, pages 226--231, 1996.
[9]
J. Fan and S. Borlase. The evolution of distribution. Power and Energy Magazine, IEEE, 7(2):63--68, 2009.
[10]
J. D. D. Glover and M. S. Sarma. Power System Analysis and Design. Brooks/Cole Publishing Co., Pacific Grove, CA, USA, 3rd edition, 2001.
[11]
J. A. Hartigan and M. A. Wong. A K-means clustering algorithm. Applied Statistics, 28:100--108, 1979.
[12]
H. Pezeshki and P. Wolfs. Correlation based method for phase identification in a three phase lv distribution network. In Universities Power Engineering Conference (AUPEC), 22nd Australasian, pages 1--7, 2012.
[13]
A. Strehl and J. Ghosh. Cluster ensembles--a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 3:583--617, 2003.

Cited By

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  • (2024)Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer DataEnergies10.3390/en1801012818:1(128)Online publication date: 31-Dec-2024
  • (2024)A Novel Power-Band Based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing IdentificationIEEE Transactions on Power Delivery10.1109/TPWRD.2024.340226339:4(2327-2339)Online publication date: Aug-2024
  • (2024)Hierarchical Clustering Method for Phase Identification in Low Voltage Distribution Networks Based on Voltage Monitoring Data2024 21st International Conference on Harmonics and Quality of Power (ICHQP)10.1109/ICHQP61174.2024.10768710(603-607)Online publication date: 15-Oct-2024
  • Show More Cited By

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cover image ACM Conferences
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2015
2378 pages
ISBN:9781450336642
DOI:10.1145/2783258
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 10 August 2015

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Author Tags

  1. clustering
  2. data mining
  3. power distribution grids
  4. topology inference
  5. voltage time series

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2024)Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer DataEnergies10.3390/en1801012818:1(128)Online publication date: 31-Dec-2024
  • (2024)A Novel Power-Band Based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing IdentificationIEEE Transactions on Power Delivery10.1109/TPWRD.2024.340226339:4(2327-2339)Online publication date: Aug-2024
  • (2024)Hierarchical Clustering Method for Phase Identification in Low Voltage Distribution Networks Based on Voltage Monitoring Data2024 21st International Conference on Harmonics and Quality of Power (ICHQP)10.1109/ICHQP61174.2024.10768710(603-607)Online publication date: 15-Oct-2024
  • (2024)Applying Sensor-Based Phase Identification With AMI Voltage in Distribution SystemsIEEE Access10.1109/ACCESS.2023.334681012(1235-1249)Online publication date: 2024
  • (2024)A review of distribution network applications based on smart meter data analyticsRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114151191(114151)Online publication date: Mar-2024
  • (2023)Permutation-based Time-Series Correlation Analysis for Customer Phase Identification from Low-Voltage Measurements2023 10th International Conference on Power and Energy Systems Engineering (CPESE)10.1109/CPESE59653.2023.10303103(90-94)Online publication date: 8-Sep-2023
  • (2023)Phase Identification in Power Distribution Systems via Feature EngineeringIEEE Access10.1109/ACCESS.2023.332644511(118615-118624)Online publication date: 2023
  • (2023)Consensus based phase connectivity identification for distribution network with limited observabilitySustainable Energy, Grids and Networks10.1016/j.segan.2023.10107034(101070)Online publication date: Jun-2023
  • (2022)A Practical Approach to Identify the Phases Sequence in Five Phase Machines with Combined Star–Pentagon ConfigurationMathematics10.3390/math1021408610:21(4086)Online publication date: 2-Nov-2022
  • (2022)Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection ProcessesIEEE Transactions on Sustainable Energy10.1109/TSTE.2022.314065013:2(948-956)Online publication date: Apr-2022
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