Anomaly Detection in Electrical Networks Using AI and Sensor Modeling
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Solution Overview
Problem
Monitoring large electrical networks is challenging due to the impracticality of monitoring every node, especially with limited sensors, and the difficulty in detecting anomalies such as electricity theft or surges in a timely manner, which can impact network performance and cost.
Innovation Solution
The system employs artificial intelligence and sensor data modeling to detect and localize anomalies, predict their impact, and determine optimal sensor placement, reducing the number of sensors required and providing real-time anomaly detection and localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every node in a large electrical network is monitored with sensors, then measurement precision and anomaly detection capability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The electrical network is divided into multiple zones or regions, with sensors strategically placed at boundary locations between zones. This segmentation allows the system to monitor entire network segments rather than requiring sensors at every individual node, reducing the total sensor count while maintaining anomaly detection capability across the full network.
Solution Approach 2:
The patent introduces a computational model that acts as an intermediary between limited sensor measurements and network-wide anomaly detection. The model uses measurements from a small number of sensors combined with network topology information to infer the state of unmonitored nodes, effectively allowing indirect monitoring of the entire network without physically placing sensors everywhere.
2Loss of information
If measurements are taken at every node in a large network, then data completeness is improved, but the amount of data to be processed increases prohibitively
Solution Approach 1:
The patent extracts and utilizes only the essential information needed for anomaly detection from the network measurements. Rather than processing all raw measurement data from every node, the system extracts key features and combines them with pre-stored network topology information, significantly reducing the computational burden while maintaining detection effectiveness.
Solution Approach 2:
The system performs preliminary actions by pre-storing network topology information and measurement data from unmonitored nodes in a database before actual anomaly detection occurs. This allows the computational model to quickly retrieve and combine this pre-prepared information with current sensor measurements, avoiding the need to process and store vast amounts of real-time data from every node during operation.
3Measurement precision
If sensors are placed throughout a large network, then anomaly localization accuracy is improved, but the cost and complexity of sensor deployment increase
Solution Approach 1:
The patent transitions from a purely spatial approach to anomaly localization (requiring sensors at specific physical locations) to a computational approach that uses network topology as an additional dimension. The system leverages the structural relationships between nodes in the network graph, combining limited spatial measurements with topological information to achieve accurate anomaly localization without requiring dense sensor deployment throughout the physical network.
Data Source
AI summary
The present subject matter enables early or real-time detection of anomalies in electric networks. In various applications, the system detects anomalies, such as electricity theft, electricity surge, etc. It solves the difficult-to-detect problems in an electrical network, where anomalies like electricity theft or electrical surge may not be found until it has raised numerous concerns or complaints, or has created a significant impact on infrastructure functionality, service quality, or cost. In addition, the present subject matter decreases the requirement for large number of sensors and provides more cost effective and scalable solutions. The present subject matter provides a method for determining where a detected anomaly is occurring within an electrical network. Variations of the present subject matter include anomaly identification systems for addressing anomalies in large networks. Various applications of the present subject matter provide guidance or effective placement of sensors in the electrical network.