Meter-to-Transformer Connectivity Using Voltage Clustering
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Solution Overview
Problem
Utility distribution grids face errors in load-to-secondary-transformer connectivity mapping due to infrequent record updates, especially with the integration of distributed energy resources, leading to inaccurate utility distribution system topology.
Innovation Solution
A system utilizing advanced metering infrastructure (AMI) meters and data processing techniques to estimate load-to-secondary-transformer connectivity by constructing matrices from time-series voltage data, applying clustering methods, and generating silhouette scores to accurately group meters into transformer groups, with the option of using LiDAR or high-resolution images for connectivity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual recording or infrequent updates of load-to-secondary-transformer connectivity records are used, then device complexity is reduced, but measurement precision and reliability of connectivity mapping deteriorate
Solution Approach 1:
The system enables automated self-updating of connectivity records by having meters continuously report their operational status and connectivity information to the data processing system, eliminating the need for manual recording while maintaining high accuracy of the connectivity mapping
Solution Approach 2:
The system implements continuous feedback loops where meters report their status, the data processing system analyzes the information, and connectivity records are automatically updated, ensuring the mapping remains accurate without increasing operational complexity
2Reliability
If continuous monitoring and frequent updates of connectivity records are implemented, then measurement precision and reliability improve, but device complexity and operational burden increase
Solution Approach 1:
Meters automatically generate and transmit their own connectivity status reports without requiring external intervention or complex centralized monitoring infrastructure, achieving continuous updates with minimal system complexity
Solution Approach 2:
The data processing system performs multiple functions including data collection, analysis, validation, and automatic record updating within a single integrated platform, reducing overall system complexity while maintaining continuous monitoring capabilities
3Ease of operation
If traditional manual record updating is used, then ease of operation is maintained, but loss of information and outdated topology data increase
Solution Approach 1:
The system automatically captures and updates connectivity information without manual intervention, preventing information loss while keeping the operation simple through automated processes that require minimal user input
Solution Approach 2:
The system maintains continuous monitoring and automatic updating of connectivity records, ensuring information remains current without requiring repeated manual operations, thus preventing information loss while maintaining operational simplicity
Data Source
AI summary
Systems and methods for meter-to-transformer connectivity. The system can include a data processing system comprising one or more processors coupled with memory. The data processing system can receive a time-series data set of voltage measured by meters at a plurality of loads in an electricity distribution grid. The data processing system can construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the meters. The data processing system can generate, via clustering techniques applied to the matrix, a silhouette score for each of the meters in the matrix to group the meters into transformer groups. The data processing system can provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the meters associated with a transformer of the transformer groups generated based on the clustering techniques applied to the matrix.