Automatic Grid Identification via Timestamp Correlation
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Solution Overview
Problem
Current power monitoring systems face challenges in automatically identifying and synchronizing data across multiple electrical grids, particularly due to independent clock operations in intelligent electronic devices (IEDs) and the complexity of manual synchronization processes, which hinders efficient data analysis and troubleshooting.
Innovation Solution
A method is introduced where a master controller communicates instructions to IEDs to store variation data of electrical characteristics, correlates the data to determine correlation values, and identifies whether IED pairs are in the same or different grids based on correlation criteria, such as peak correlation values and offset differences, to automatically identify independent electrical grids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS time systems are used to synchronize IEDs, then data synchronization accuracy is improved, but hardware cost and system complexity increase
Solution Approach 1:
The patent extracts the time synchronization function from the GPS hardware system and implements it through software-based timestamp correlation algorithms. The master controller compares timestamps from IEDs without requiring physical GPS receivers at each device, thereby achieving synchronization accuracy while eliminating complex hardware installations.
Solution Approach 2:
The patent introduces a master controller as an intermediary that coordinates synchronization between IEDs. Instead of direct GPS connection to each IED, the master controller receives timestamps from all IEDs, performs correlation analysis, and establishes synchronization relationships, thereby reducing hardware complexity while maintaining accuracy.
2Device complexity
If manual synchronization methods are used, then hardware cost is reduced, but operational complexity and expertise requirements increase
Solution Approach 1:
The patent implements automated synchronization where the master controller autonomously performs timestamp correlation and grid identification without requiring manual intervention. The system self-adjusts synchronization parameters and automatically identifies IEDs on different grids, eliminating the need for operator expertise while keeping hardware simple.
Solution Approach 2:
The patent changes the synchronization approach from manual parameter adjustment to automated parameter optimization. The master controller automatically analyzes timestamp variations, determines optimal synchronization offsets, and adjusts timing parameters dynamically, thereby simplifying operation while maintaining low hardware requirements.
3Adaptability or versatility
If IEDs operate with independent clocks, then device autonomy is improved, but data correlation accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where IEDs send their timestamps to the master controller, which analyzes the correlations and provides synchronization information back to the IEDs. This allows IEDs to maintain operational autonomy with independent clocks while achieving accurate event correlation through continuous feedback on timing relationships.
Solution Approach 2:
The patent performs preliminary timestamp collection and correlation analysis before final synchronization decisions are made. The master controller gathers timestamps from all IEDs, pre-analyzes the correlations to identify patterns and offsets, and then establishes synchronization relationships, thereby maintaining autonomy while ensuring accuracy through advance preparation.
4Adaptability or versatility
If multiple independent grids are monitored, then system coverage is improved, but data synchronization difficulty increases
Solution Approach 1:
The patent segments the monitoring system into grid-specific analysis groups. The master controller identifies which IEDs belong to which electrical grids based on timestamp correlation patterns, and performs synchronization analysis separately for each grid segment. This segmentation allows multi-grid monitoring capability while keeping the synchronization logic simple and manageable for each individual grid.
Data Source
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AI summary
A method of automatically identifying whether intelligent electronic devices (IEDs) in a power monitoring system are in multiple electrical grids. A controller sends an instruction to each IED in a predetermined time sequence such that each IED receives the instruction at a different time, commanding each IED to begin logging variation data indicative of frequency variations in a current/voltage signal monitored by the IED and to send the variation data to the controller and an associated cycle count of a point in the current/voltage signal. The controller receives the variation data and associated cycle count and determines a peak correlation using a data alignment algorithm on IED pair combinations. If the IEDs are on the same electrical grid, the peak correlations should occur at cycle count offsets that match the order that the IEDs received the instruction. Any discrepancies in the expected order of peak correlations are flagged, and the corresponding IEDs are determined to be on different grids.