Transformer Voltage Estimation for Data Quality Error Detection
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Solution Overview
Problem
Existing methods for monitoring transformers fail to accurately identify overloaded transformers due to record errors, misassignments, and energy diversion, leading to potential fires and service quality issues.
Innovation Solution
A system that estimates high-side voltage of transformers using smart meter data, impedance assumptions, and clustering analysis to identify discrepancies and alert users to potential overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing monitoring methods are used to track peak coincident meter load, then monitoring simplicity is maintained, but measurement precision of transformer loading status deteriorates due to record errors and misassignments
Solution Approach 1:
The patent introduces an intermediary system that uses smart meter data and voltage measurements as intermediate indicators to infer transformer loading status. Instead of directly measuring transformer load, the system uses voltage drop across the transformer as a mediator to determine loading conditions, which resolves the issue of inaccurate direct load measurements while maintaining practical monitoring capabilities
Solution Approach 2:
The patent replaces direct mechanical/electrical load measurement systems with a computational approach using voltage measurements and data analysis. By substituting direct load sensing with voltage-based inference and algorithmic analysis of smart meter data, the system achieves more accurate loading status detection without requiring complex direct measurement infrastructure
2Reliability
If transformer overload monitoring is improved to detect data quality errors, then reliability of transformer operation is enhanced, but difficulty of detecting and measuring increases due to multiple error sources
Solution Approach 1:
The patent segments the monitoring approach by analyzing different data sources separately - smart meter load data, voltage measurements, and transformer records - and then comparing them to identify discrepancies. This segmentation allows the system to detect errors in specific data sources without being overwhelmed by the complexity of analyzing all error sources simultaneously
Solution Approach 2:
The system implements feedback mechanisms where voltage measurements and smart meter data continuously inform the monitoring system about actual transformer loading conditions. This feedback loop enables the system to detect when recorded data diverges from actual conditions, allowing for real-time identification and correction of data quality issues affecting transformer reliability
3Measurement precision
If voltage estimation and clustering analysis are performed to identify discrepancies, then measurement precision of transformer status is improved, but loss of time for data processing increases
Solution Approach 1:
The patent applies partial action by performing clustering and voltage estimation analysis selectively on groups of transformers rather than individually analyzing each transformer in isolation. By processing transformers in clusters with similar characteristics, the system achieves high measurement precision while significantly reducing the total processing time required
Solution Approach 2:
The system performs preliminary actions by pre-grouping transformers into clusters based on their electrical characteristics and geographic locations before conducting detailed voltage estimation and discrepancy analysis. This preliminary clustering prepares the data structure to enable faster, more efficient processing during actual monitoring operations
Data Source
AI summary
In some embodiments, the disclosure is directed to a system for estimating high-side voltage of transformers in an electrical distribution network. In some embodiments, the system executes program instructions that enable one or more computers to receive meter load and/or voltage data from smart meters. In some embodiments, the data is used in transformer load analysis to assume impedance values, determining low-side voltage, and estimating high-side voltage using the determined low-side voltage. In some embodiments, the system is configured to these estimations against known values to identify discrepancies, which are output via a graphical user interface. In some embodiments, the system is configured to determine expected percent impedance from historical data and/or estimate impedance characteristics. In some embodiments, the system is configured to execute clustering of voltage estimates to identify similar phases among neighboring transformers.