Intermediate Node Data Filtering for AMI Systems
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Solution Overview
Problem
Advanced Metering Infrastructure (AMI) systems face challenges in reducing data transmission volume, providing operators with actionable consumption insights, and identifying anomalies in sensor data, leading to potential overloading and inefficiencies in data processing.
Innovation Solution
Implementing an intermediate node system that compares actual sensor data with expected values, transmitting only data that differs from expectations, and using clustering techniques to represent data as n-dimensional points, allowing for reduced data transmission and processing, while also detecting errors and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all sensor data is transmitted to the operator node, then complete monitoring information is provided, but data transmission bandwidth and power consumption increase significantly
Solution Approach 1:
The patent extracts only the essential information from sensor data by comparing actual readings with expected values. Only deviations from expected patterns are transmitted to the operator node, while normal operational data is filtered out. This extraction principle reduces transmission volume while preserving critical monitoring information.
Solution Approach 2:
The system performs preliminary processing of sensor data at intermediate nodes before transmission. Expected values are pre-calculated based on historical patterns, and data comparison is performed in advance, so that only relevant deviations need to be transmitted. This preliminary action eliminates the need to transmit complete raw datasets.
2Loss of information
If all sensor data is transmitted to the operator node, then complete monitoring information is provided, but data transmission volume increases leading to network overload
Solution Approach 1:
The patent extracts only the essential information from sensor data by comparing actual readings with expected values. Only deviations from expected patterns are transmitted to the operator node, while normal operational data is filtered out. This extraction principle reduces transmission volume while preserving critical monitoring information.
Solution Approach 2:
The system performs preliminary processing of sensor data at intermediate nodes before transmission. Expected values are pre-calculated based on historical patterns, and data comparison is performed in advance, so that only relevant deviations need to be transmitted. This preliminary action eliminates the need to transmit complete raw datasets.
3Loss of information
If all sensor data is transmitted to the operator node, then complete monitoring information is provided, but data processing time and computational load increase
Solution Approach 1:
The patent divides the data processing function into multiple segments: intermediate nodes perform local comparison with expected values and filter data, while the operator node processes only the filtered deviations. This segmentation distributes computational load and reduces the processing time at the central operator node.
Solution Approach 2:
The system performs preliminary processing of sensor data at intermediate nodes before transmission. Expected values are pre-calculated based on historical patterns, and data comparison is performed in advance, so that only relevant deviations need to be transmitted. This preliminary action eliminates the need to transmit complete raw datasets.
Data Source
AI summary
An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and relay a subset of the data that is not according to an expected value. The expected value may be determined based on a forecasting function computed based on past data. The expected value may be a spatial shape in an n-dimension space. A data not within the spatial shape may be considered not in accordance with the expected value. In some case, the spatial shape is defined by a centroid and a radius. The spatial shape may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of spatial shapes of each group over time.