Smart Meter Voltage Variance Analysis for Non-Technical Line Loss Detection
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Solution Overview
Problem
Electric utility companies face difficulties in identifying sources of non-technical line loss, such as electricity theft, due to the lack of effective methods to correlate consumption and voltage fluctuations in existing systems.
Innovation Solution
A system utilizing smart meter data to collect and analyze hourly voltage and consumption data, calculating voltage variances and correlating them with consumption patterns to flag potential instances of electricity theft by comparing intervals with high voltage variances and consumption, thereby identifying likely sources of non-technical line loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used, then system simplicity is maintained, but the ability to identify non-technical line loss is insufficient
Solution Approach 1:
The system segments the monitoring task by separately collecting voltage data and consumption data from smart meters, then processing them through distinct analytical paths (voltage variance calculation, consumption pattern analysis) before correlating results to identify line loss sources
Solution Approach 2:
The system introduces an intermediary analysis layer that correlates voltage and consumption data to detect anomalies indicating non-technical line loss, acting as a mediator between raw meter data and theft detection conclusions
2Measurement precision
If detailed voltage and consumption data collection is implemented, then theft detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary processing of voltage and consumption data separately before correlation - calculating voltage variances and establishing consumption patterns in advance, which simplifies the final theft detection analysis by pre-computing key indicators
Solution Approach 2:
The system collects more data than minimally required (both voltage and consumption data at multiple time intervals) to ensure sufficient information for accurate theft detection, accepting the benefit of higher detection accuracy despite increased data processing requirements
Data Source
AI summary
Systems, methods, and other embodiments associated with identifying non-technical line loss using data from smart meters in an electric grid are described. In one embodiment, a method includes querying a utility database to collect meter data, wherein the meter data is from electric meters connected to a transformer in an electric grid. Querying the utility database includes collecting the data according to a plurality of intervals over a period of time. Electric consumption and voltage variances are analyzed for the set of meters to identify a first set of intervals that satisfy a threshold for electric consumption and to identify a second set of intervals that satisfy a threshold for voltage variances. The first set of intervals is compared with the second set of intervals to determine whether the set of meters are associated with non-technical line loss.