Distributed Grid Sensor Network for Outage-Resilient Anomaly Detection
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Solution Overview
Problem
Current monitoring technologies for power grids face limitations due to high costs, delayed data reporting, loss of power and communication during grid failures, and fragmentation issues caused by multiple power companies, leading to inadequate situational awareness and delayed anomaly detection.
Innovation Solution
A system of broadly distributed, secure, cloud-supported sensors connected via a communications network that reports measurements to a central processing application, enabling real-time data collection, analysis, and anomaly detection across various geographies, with a method for determining consensus among sensors to improve network metrics and situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring equipment is installed at power substations, then data collection coverage is achieved, but data reporting is delayed and power is lost during grid failures
Solution Approach 1:
The patent divides the monitoring system into distributed sensor nodes deployed at customer premises rather than centralized substation equipment. Each sensor independently collects and reports data, eliminating the single point of failure and delay inherent in centralized monitoring. This segmentation allows continuous data collection even when grid power is interrupted at specific locations.
Solution Approach 2:
The patent introduces communication network infrastructure (cell towers, fiber optic networks) as intermediaries between sensors and central processing. These intermediaries provide alternative power sources and communication pathways that remain operational during grid failures, enabling uninterrupted data transmission from sensors to the central platform.
2Measurement precision
If more sensors are deployed to improve data resolution, then measurement precision increases, but system cost and complexity increase
Solution Approach 1:
The patent designs sensors with multiple functions: they monitor both power grid parameters (voltage, current, frequency) and communication network parameters (signal strength, latency). This multi-functionality allows a single deployed device to provide diverse data streams, increasing measurement precision without proportionally increasing system complexity or deployment costs.
Solution Approach 2:
The patent combines power sensing capabilities with existing communication infrastructure equipment. By merging these functions into unified sensor nodes that leverage shared power and communication resources, the system achieves high-resolution monitoring across multiple parameters while avoiding the complexity of separate dedicated systems for each measurement type.
3Loss of information
If distributed sensors are deployed across multiple locations, then situational awareness improves, but data fragmentation and communication challenges increase
Solution Approach 1:
The patent implements feedback mechanisms where sensors not only report data but also receive configuration updates and anomaly detection alerts from the central platform. This bidirectional communication enables dynamic adaptation of sampling rates and thresholds based on grid conditions, improving situational awareness while automating data management to reduce operational complexity.
Solution Approach 2:
The patent employs preliminary data processing and filtering at the sensor node level before transmission. Sensors perform local anomaly detection and prioritize critical data for immediate reporting, while deferring routine measurements to scheduled transmissions. This preliminary action reduces communication overhead and simplifies central data management by pre-processing information at the source.
Data Source
AI summary
A system and method for data collection and aggregation using a distributed network of communications enabled sensors connected to another primary network to achieve a secondary out-of-band monitoring perspective, for example, in power grids. The data collection system includes an aggregation and processing server configured to collect data from a variety of sensors adjacent to the monitored network each sensor includes secondary power such that it can continue data transmission even during power grid outages. The data collection system includes a method for secure real-time data ingest, machine learning enabled analysis, risk assessment, and anomaly detection on a broad geographic scale irrespective of isolated network boundaries.