Use of smart-meter data to determine distribution system topology

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.

[1]  Victor C. M. Leung,et al.  Electricity Theft Detection in AMI Using Customers’ Consumption Patterns , 2016, IEEE Transactions on Smart Grid.

[2]  Tom A. Short,et al.  Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling , 2013, IEEE Transactions on Smart Grid.

[3]  Andrew John Berrisford.  A tale of two transformers: An algorithm for estimating distribution secondary electric parameters using smart meter data , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[4]  Houman Pezeshki,et al.  Consumer phase identification in a three phase unbalanced LV distribution network , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[5]  Stamatis Karnouskos,et al.  The Impact of Smart Grid Prosumer Grouping on Forecasting Accuracy and Its Benefits for Local Electricity Market Trading , 2014, IEEE Transactions on Smart Grid.

[6]  Saverio Bolognani,et al.  Identification of power distribution network topology via voltage correlation analysis , 2013, 52nd IEEE Conference on Decision and Control.

[7]  Santiago Grijalva,et al.  Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness , 2015, IEEE Transactions on Smart Grid.

[8]  M. F. McGranaghan,et al.  Automatic identification of service phase for electric utility customers , 2011, 2011 IEEE Power and Energy Society General Meeting.

[9]  Santiago Grijalva,et al.  Increasing distribution system model accuracy with extensive deployment of smart meters , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[10]  J.E.R. Alves,et al.  Identification of energy theft and tampered meters using a central observer meter: a mathematical approach , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[11]  Nelson J. Trujillo-Barreto,et al.  On the Fisher's Z transformation of correlation random fields , 2009 .

[12]  Wenpeng Luan,et al.  Distribution network topology error correction using smart meter data analytics , 2013, 2013 IEEE Power & Energy Society General Meeting.

[13]  Neville R. Watson,et al.  Smart meters to monitor power quality at consumer premises , 2015 .

[14]  Houman Pezeshki,et al.  Correlation based method for phase identification in a three phase LV distribution network , 2012, 2012 22nd Australasian Universities Power Engineering Conference (AUPEC).

[15]  Vijay Arya,et al.  Voltage-based clustering to identify connectivity relationships in distribution networks , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[16]  Bing Wang,et al.  Generate distribution circuit model using AMI data , 2014, 2014 China International Conference on Electricity Distribution (CICED).

[17]  Kameshwar Poolla,et al.  Phase identification in distribution networks with micro-synchrophasors , 2015, 2015 IEEE Power & Energy Society General Meeting.

[18]  Waree Kongprawechnon,et al.  Cluster analysis for primary feeder identification using metering data , 2015, 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[19]  Wenpeng Luan,et al.  Smart Meter Data Analytics for Distribution Network Connectivity Verification , 2015, IEEE Transactions on Smart Grid.

[20]  Lingfeng Wang,et al.  Support vector machine based data classification for detection of electricity theft , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.