Use of smart-meter data to determine distribution system topology

Abstract

Smart-meter data presents an opportunity for utilities to improve their database records, and develop a low-voltage (LV) model which may be useful for outage management and fault detection, isolation and response, phase balancing, and network planning. In addition, impact assessment studies on new technologies can be performed. This study presents several contributions in the area of determining the topology of the LV distribution system. This is in terms of identifying the transformer a particular installation control point is connected to, and the phase if that customer is single-phase. First, harmonic voltage correlation is proposed as it is more robust to noise and missing records than the prior algorithm of voltage correlation. Second, it is demonstrated that smart-meter data can be used to determine the transformer/phase to which a customer is connected and update database records in this regard. To achieve this, a new algorithm based on correlation analysis with the Fisher Z transform is developed. Third, a method to estimate LV feeder and service main impedances is presented. Further work is necessary; however, the results from trials in Auckland, New Zealand are highly promising.

Contributors

Neville R. Watson

Journal

Journal ID (publisher-id): JOE

Title: The Journal of Engineering

Abbreviated Title: J Eng

Publisher: The Institution of Engineering and Technology

ISSN (Print):

ISSN (Electronic): 2051-3305

Publication date Collection: May 2016

Publication date (Electronic preprint): 22 March 2016

Publication date (Electronic): 03 May 2016

Volume: 2016

Issue: 5

Pages: 94-101

Affiliations

[1 ] Electrical and Computer Engineering Department, University of Canterbury , Private Bag 4800, Christchurch, New Zealand

[2 ] Vector Network Ltd. , Auckland, New Zealand

Article

Publisher ID: JOE.2016.0033 Manuscript ID: JOE.2016.0033

DOI: 10.1049/joe.2016.0033

License:

This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License ( http://creativecommons.org/licenses/by-nd/3.0/)

Product

Self URI (journal page): https://digital-library.theiet.org/content/journals/

Categories

Subject: en-gtd