Meter Accuracy Verification via Hierarchical Power Comparison
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Solution Overview
Problem
Power meters in hierarchical electrical distribution systems experience accuracy drift over time, leading to unreliable readings, and existing methods fail to efficiently identify which meters need recalibration without requiring frequent and potentially unnecessary calibrations.
Innovation Solution
A system and method that calculate comparison values and difference values across multiple levels of power meters in a hierarchy to identify meters out of calibration, allowing for targeted recalibration and reducing the frequency of calibrations needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If power meters are frequently calibrated to maintain accuracy, then measurement precision is improved, but loss of time and productivity deteriorate due to meters being pulled out of service
Solution Approach 1:
The system enables self-verification of meter accuracy by automatically comparing measurements from multiple meters in a hierarchy against expected relationships (e.g., parent node power should equal sum of child node powers). This eliminates the need for manual calibration interventions while maintaining measurement precision through continuous automated monitoring and identification of meters requiring calibration.
Solution Approach 2:
The system implements continuous feedback by monitoring meter measurements and automatically identifying when a meter's reading deviates from expected values based on hierarchical relationships. This feedback mechanism triggers calibration alerts only when necessary, reducing unnecessary downtime while maintaining accuracy through timely interventions.
2Measurement precision
If individual meter calibration is performed to ensure accuracy, then measurement precision is improved, but device complexity increases due to manual identification and calibration processes
Solution Approach 1:
The system automatically performs the complex task of identifying which meters need calibration by analyzing hierarchical relationships between meters and comparing measurements. This self-identification process eliminates manual intervention and reduces the complexity of calibration management while maintaining high measurement precision through systematic analysis.
Solution Approach 2:
The system divides the calibration management task into segments by evaluating each meter independently against hierarchical expectations. This segmentation allows the complex problem of fleet-wide calibration management to be broken down into simple, automated comparisons for individual meters, reducing overall system complexity.
3Reliability
If all meters are calibrated to maintain system accuracy, then reliability is improved, but loss of time and productivity worsen due to extensive calibration requirements
Solution Approach 1:
The system continuously self-monitors the accuracy of all meters through automated hierarchical comparisons, identifying only those meters that have drifted from expected values. This approach maintains high reliability by detecting accuracy issues in real-time while minimizing calibration time by calibrating only the specific meters that need it, rather than performing blanket calibrations.
Solution Approach 2:
The system implements periodic automated verification of meter accuracy through continuous monitoring and comparison of hierarchical measurements. This periodic action maintains reliability by regularly checking meter performance and triggering calibration only when accuracy drift is detected, rather than requiring frequent universal calibrations.
Data Source
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AI summary
Systems for identifying a meter that is out of calibration and methods of controlling the same include obtaining a power measurement value for each of a plurality of metering devices in a hierarchy of metering devices, calculating virtual metering points for a candidate metering device using metering devices connected upstream and/or downstream to the candidate metering device, and identifying the candidate metering device as being out of calibration by leveraging the virtual metering points and the candidate metering device's specification.