Early detection of meter faults and hazards using electricity smart meter data

Masters Thesis

Hartig-Franc, Gabriella. 2023. Early detection of meter faults and hazards using electricity smart meter data. Masters Thesis Master of Professional Studies (Research). University of Southern Queensland. https://doi.org/10.26192/z4vz6
Title

Early detection of meter faults and hazards using electricity smart meter data

TypeMasters Thesis
AuthorsHartig-Franc, Gabriella
Supervisor
1. FirstProf Karen Trimmer
2. SecondProf Paul Wen
3. ThirdLukus McGowan
Institution of OriginUniversity of Southern Queensland
Qualification NameMaster of Professional Studies (Research)
Number of Pages138
Year2023
PublisherUniversity of Southern Queensland
Place of PublicationAustralia
Digital Object Identifier (DOI)https://doi.org/10.26192/z4vz6
Abstract

Electrical smart meters (also known as advanced meters, or “type 4 meters”) present the potential for improved public safety outcomes through the collection of real-time big data, which could be used to remotely identify defects or abnormal operating conditions within the meter. Meter internal temperature could be a useful measure for this as it is simple to obtain. High internal temperatures are associated with range of issues within the meters and can damage electrical components over time, reducing the operating life of the unit.
There is currently a lack of research relating to smart meter operational temperature behaviour, and this is linked to a current lack of publicly available operational meter data. This practice based, exploratory research project aimed to answer the following research problem: “To improve the safety of smart meters through early prediction of fault conditions, what are the temperature related behaviours within a smart meter while operating normally, in a range of environmental and usage conditions?”
To achieve this, operational temperature data was analysed from 2478 in-service, operational smart meters over a nine-month period (from August 2019 to May 2020) to determine the proportion of meters which exceeded manufacturer operating limits. Additionally, each data point was linked by location, time and date to the closest available Bureau of Meteorology Weather station to compare internal meter temperature with ambient air temperature.
The research was able to identify the proportion of meters within the population which exceeded operational temperature limits. The research was also able to determine the difference in mean meter and air temperature at different times of the day as well as in cases where operational temperature limits were exceeded.

KeywordsPreventative maintenance; smart metering; remote monitoring; big data; smart grid; temperature monitoring
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400899. Electrical engineering not elsewhere classified
Public Notes

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Byline AffiliationsSchool of Education
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