Distributed Smart Grid Processing via Edge Node Segmentation
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Solution Overview
Problem
Conventional smart grid infrastructure faces challenges with data overload, leading to throughput issues and obsolete processing hardware due to the rapid growth of data being transmitted and processed, which cannot scale as quickly as the data generation rate.
Innovation Solution
Implementing a distributed processing method across a network of nodes, where each node executes stream functions on time series data to generate data streams, processing them in real-time and sharing the results, thereby breaking down complex processing into granular steps across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized processing infrastructure is used to handle smart grid data, then data processing capability is improved, but infrastructure complexity and cost increase rapidly as data volume grows
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge computing nodes deployed throughout the smart grid infrastructure. Each edge node processes data locally, dividing the processing workload across multiple locations rather than concentrating it in a single centralized system, thereby reducing infrastructure complexity while maintaining processing capability.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge computing nodes at multiple physical locations within the smart grid. This transforms the processing architecture from a single-point centralized model to a distributed spatial network, allowing data to be processed closer to its source and reducing the burden on centralized infrastructure.
2Loss of information
If all smart meter data is transmitted to the back office for processing, then comprehensive data analysis is improved, but network traffic and throughput issues worsen
Solution Approach 1:
The patent extracts processing functions from the centralized back office and places them at the edge of the network where data is generated. Edge nodes perform local data processing, filtering, and aggregation, extracting only essential information for transmission to the back office. This reduces network traffic load while preserving the completeness of data analysis through distributed processing.
Solution Approach 2:
The patent implements preliminary data processing at the edge before data is transmitted to the back office. Edge nodes perform initial filtering, aggregation, and preprocessing of smart meter data, preparing it in advance for centralized analysis. This preliminary action reduces the volume of data requiring network transmission while ensuring comprehensive analysis capabilities are maintained at the back office.
3Power
If centralized processing hardware is used, then processing power is improved, but hardware obsolescence occurs faster as data volume grows
Solution Approach 1:
The patent segments processing power across multiple distributed edge nodes rather than concentrating it in centralized hardware. This distribution allows the system to scale by adding individual nodes as needed, preventing any single piece of hardware from becoming a bottleneck and extending the effective lifespan of the processing infrastructure through modular expansion.
Solution Approach 2:
The patent enables edge nodes to perform processing locally, making the system self-sufficient at the network edge. Each edge node independently handles data processing for its local area, reducing dependency on centralized processing hardware and allowing the system to scale without requiring continuous upgrades to central processing facilities.
Data Source
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AI summary
Nodes within a wireless mesh network are configured to monitor time series data associated with a utility network, including voltage fluctuations, current levels, temperature data, humidity measurements, and other observable physical quantities. The nodes execute stream functions to process the recorded time series data and generate data streams. The node is configured to transmit generated data streams to neighboring nodes. A neighboring node may execute other stream functions to process the received data stream(s), thereby generating additional data streams. A server coupled to the wireless mesh network collects and processes the data streams to identify events occurring within the network.