Power Cycle Signal Matching for User-Transformer Identification
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Solution Overview
Problem
Existing user-transformer relationship identification methods in power networks suffer from low accuracy, leading to misidentification and an unreliable basis for calculating line loss electricity.
Innovation Solution
A signal processing method that utilizes power frequency cycle feature data collected by secondary and primary nodes in the same time period to determine the target primary node, incorporating similarity degrees, SNR values, and attenuation values to enhance identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If power frequency cycle feature data collected by primary nodes in different time periods is used for identification, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The system performs preliminary synchronization of time periods before comparing power frequency cycle feature data. The secondary node and primary nodes align their data collection time windows in advance, ensuring that comparisons are made between data from the same time period. This preliminary time synchronization action enables accurate identification while maintaining feasible device complexity.
2Measurement precision
If power frequency cycle feature data collected in the same time period is used, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The system implements periodic data collection cycles where the secondary node and primary nodes simultaneously collect power frequency cycle feature data in synchronized time periods. By organizing data collection into regular periodic cycles with predefined time windows, the system achieves high identification accuracy through same-period comparisons while controlling overall processing time through efficient cyclic operation.
3Measurement precision
If multiple primary nodes' data is compared, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The system extracts and compares only the essential power frequency cycle feature data from multiple primary nodes, focusing on key identification characteristics rather than processing all available data. The secondary node extracts relevant features from each primary node's data set and performs targeted comparisons, achieving high identification accuracy while reducing processing complexity by taking out only the necessary comparison elements.
Data Source
AI summary
This application provides a signal processing method and associated apparatuses. The method includes: a secondary node obtains first data and a plurality of pieces of second data; then, the secondary node determines a plurality of first similarity degrees based on the first data and the plurality of pieces of second data; and then, the secondary node determines a target primary node based on the plurality of first similarity degrees. The first data is power frequency cycle feature data collected by the secondary node in a target time period, the plurality of pieces of second data are power frequency cycle feature data respectively collected by a plurality of primary nodes in the target time period.