Electricity Meter Phase Identification via Voltage Time-Series Clustering
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Solution Overview
Problem
Inaccurate phase-balancing in electrical grids due to outdated records of customer meter connections leads to operational inefficiencies, increased equipment failure, and delayed power outage management, as linemen often fail to update phase information when moving meters between phases.
Innovation Solution
A clustering algorithm is employed to identify electrical phases by analyzing voltage time series data from electricity meters, using Pearson correlation coefficients and agglomerative clustering methods to determine phase correlations and assign predicted phases, which can be compared to existing meter-phase connectivity records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual phase recording methods are used by utility companies, then operational simplicity is maintained, but phase-balancing accuracy deteriorates due to outdated records when linemen move meters
Solution Approach 1:
The system enables self-service by allowing the infrastructure to automatically update its own phase information. Meters continuously monitor voltage waveforms and automatically determine their phase assignments through correlation analysis, eliminating the need for manual updates by linemen and ensuring records remain current without additional operational complexity
Solution Approach 2:
The patent replaces the mechanical/manual system of phase recording with an automated electronic system. Instead of relying on manual documentation, the system uses voltage waveform analysis and correlation algorithms to automatically identify and record phase assignments, substituting physical measurement and manual entry with computational analysis
2Productivity
If automated meter reading infrastructure is deployed, then data collection capability is improved, but phase identification accuracy deteriorates when connection records are incorrect
Solution Approach 1:
The system implements feedback by continuously monitoring voltage waveforms at each meter and comparing them against expected phase patterns. When discrepancies are detected or when record accuracy is questioned, the system automatically recalculates phase assignments based on real-time waveform correlation, using the collected data to verify and correct phase information
Solution Approach 2:
The patent applies preliminary action by pre-establishing voltage waveform templates for each phase and preparing correlation analysis frameworks before field operations. This allows the system to immediately and accurately identify phases when new meters are installed or moved, without requiring manual measurement or documentation during the actual installation process
Data Source
AI summary
A method, apparatus, and system for identifying electrical phases connected to electricity meters are disclosed. Voltage time series data of electricity meters are collected over a preselected collection time period, and three initial kernels representing three line-to-neutral phases are generated based on voltage correlations of meter-to-meter combinations. Three new kernels are then generated based on correlation values calculated for each of the three initial kernels with each electricity meter, and electricity meters are clustered into three groups based on average correlation values associated with each electricity meter. Six new kernels representing six phases are then formed based on the average correlation value associated with each electricity meter, and a predicted phase is assigned to each electricity meter based on correlation values of the electricity meter with each of the six new kernels based on the voltage time series data.