Non-Contact Energy Anomaly Detection Using EM Field Clustering
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Solution Overview
Problem
Existing anomaly detection systems in energy systems are invasive, unreliable, and limited by the need for pre-defined anomaly categories, failing to detect anomalies outside these categories.
Innovation Solution
A non-invasive system using electromagnetic sensors to detect anomalies in energy systems by analyzing time- and frequency-domain signals from electric and magnetic field sensors, employing density-based spatial clustering (DBSCAN) to identify anomalies without pre-defined categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If potential and current transformers are used to capture input signals, then anomaly detection capability is improved, but installation difficulty and cost increase significantly due to required insulation and isolation measures
Solution Approach 1:
The patent replaces the mechanical/electrical connection system (transformers requiring physical contact and isolation) with an electromagnetic sensing system. Electromagnetic sensors detect voltage and current through field coupling without direct contact, eliminating the need for insulation and isolation measures while maintaining detection capability.
Solution Approach 2:
The patent introduces electromagnetic fields as an intermediary between the sensors and the energized conductors. The sensors detect anomalies by measuring electromagnetic fields generated by voltage and current, allowing non-contact measurement and avoiding direct electrical connection requirements.
2Device complexity
If pre-defined anomaly categories are used for detection, then device complexity is reduced, but the system becomes unable to identify anomalies outside the pre-defined categories
Solution Approach 1:
The patent implements a dynamic anomaly detection system that automatically adapts to different anomaly types without requiring pre-definition. The system learns normal operation patterns and dynamically identifies deviations, allowing it to detect both known and previously unknown anomaly categories while maintaining manageable complexity through automated adaptation.
Solution Approach 2:
The system performs self-analysis of the electrical power network by automatically establishing baseline patterns of normal operation and autonomously detecting anomalies that deviate from these patterns. This eliminates the need for manual pre-analysis and pre-definition of anomaly categories, enabling the system to handle diverse anomaly types independently.
3Device complexity
If voltage monitoring only is implemented, then device complexity is reduced, but detection capability is limited without current flow information
Solution Approach 1:
The patent combines voltage and current monitoring capabilities into a single integrated system. Electromagnetic sensors simultaneously detect both voltage (through electric field coupling) and current (through magnetic field coupling), providing comprehensive power quality analysis without significantly increasing device complexity.
Solution Approach 2:
The electromagnetic sensing system performs multiple functions simultaneously - detecting voltage, detecting current, and analyzing power quality - using the same sensor platform. This multi-functionality approach allows comprehensive monitoring without requiring separate dedicated devices for each measurement type.
4Quantity of substance
If log files with limited disturbance events are used for analysis, then data storage requirements are reduced, but only certain power quality disturbances can be detected
Solution Approach 1:
The system continuously establishes and updates baseline patterns of normal operation from ongoing measurements, enabling real-time detection of anomalies without relying on pre-stored disturbance templates. This approach allows detection of any disturbance type that deviates from normal patterns, eliminating the limitation of finite log file entries while maintaining efficient data usage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables autonomous condition monitoring of energy systems, detecting anomalies such as power quality failures without being invasive, costly, or requiring pre-defined categories, thus ensuring stable and efficient power delivery.
Implementation Method 1
Non-contact sensing of an energy system based on magnetic field uses a non-contact magnetic field sensor to produce a magnetic field signal
Implementation Method 2
Non-contact sensing of an energy system based on electric field uses a non-contact electric field sensor to produce an electric field signal
Data Source
AI summary
A method and system are provided for anomaly detection in energy systems. Non-contact sensing of an energy system based on electric and magnetic fields uses non-contact electric- and magnetic-field sensors to produce electric- and magnetic-field signals. The electric and magnetic field signals are filtered to remove noise. Features are extracted and normalized from the magnetic and electric field signals to characterize parameters of each signal. Density-based spatial clustering of extracted features is performed using a selected minimum number of points required to form a cluster and a parameter indicating the distance within which data are considered to fall within the cluster. An anomaly is determined from data point(s) that do not fall within the cluster formed by data points in normal operation. The density-based spatial clustering of extracted features may be performed using a Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. Features may be extracted using Fourier analysis.