Power Frequency Signal Matching for User-Transformer Identification
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Solution Overview
Problem
The existing user-transformer relationship identification methods in power networks have low accuracy, leading to misidentification of user-transformer relationships and unreliable calculations of line loss electricity.
Innovation Solution
A signal processing method that determines a target primary node by analyzing power frequency cycle feature data collected by secondary and primary nodes in the same time period, using similarity degrees and additional information like SNR and attenuation values to improve identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel estimation methods are used in MIMO-OFDM systems, then system complexity is reduced, but channel estimation accuracy deteriorates due to frequency selectivity and multipath fading
Solution Approach 1:
The channel estimation process is divided into multiple stages: initial rough estimation using pilot symbols, followed by iterative refinement using decision-directed estimation. The channel frequency response is segmented into multiple frequency regions that are estimated and combined separately, improving overall accuracy while managing complexity.
Solution Approach 2:
An intermediate channel estimate is generated from pilot symbols and used as a basis for decision-directed estimation. This intermediate result serves as a mediator that bridges the gap between coarse pilot-based estimation and fine decision-directed refinement, enabling accurate channel tracking without requiring direct complex processing.
2Measurement precision
If pilot symbols are densely distributed to improve channel estimation, then estimation accuracy improves, but data transmission rate deteriorates due to reduced data carrying capacity
Solution Approach 1:
Instead of using dense pilot distribution, the method applies partial action by using a sparse pilot pattern combined with decision-directed estimation that leverages the redundancy in coded data. This approach achieves sufficient channel estimation accuracy without excessively reducing the data carrying capacity, maintaining a better balance between estimation quality and transmission rate.
Solution Approach 2:
The system uses the transmitted data itself (through decision-directed estimation) to improve channel estimation accuracy. The received signals, after initial processing, are used to generate channel estimates that further refine the equalization, creating a self-improving loop that reduces reliance on dense pilot symbols.
3Measurement precision
If decision-directed estimation is used to improve accuracy, then channel tracking improves, but error propagation risk increases in low signal-to-noise ratio conditions
Solution Approach 1:
The method prepares a cushioning mechanism by using pilot-based initial estimation to establish a reliable baseline before engaging decision-directed estimation. This preliminary accurate estimate acts as a cushion that prevents error propagation from taking hold, especially in low SNR conditions where decision-directed methods alone would be unreliable.
Solution Approach 2:
The system implements feedback control where the reliability of decision-directed estimation is continuously monitored. When error propagation is detected or SNR is low, the system reduces reliance on decision-directed estimates and increases weighting toward pilot-based estimates, creating a feedback loop that maintains reliability while optimizing tracking accuracy.
Data Source
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AI summary
Embodiments of this application provide a signal processing method and apparatus, and relate to the field of power technologies, to effectively improve accuracy of user-transformer relationship identification. 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 power frequency cycle feature data is used to indicate a cycle feature of a power grid working frequency, 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, the plurality of pieces of second data are in a one-to-one correspondence with the plurality of primary nodes, the plurality of first similarity degrees are in a one-to-one correspondence with the plurality of primary nodes, and the target primary node is one of the plurality of primary nodes.