Anomaly detection in energy systems
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United States of America
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Feb 11, 2025
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Nov 24, 2022
app pub date -
Sep 12, 2019
filing date -
Sep 12, 2019
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Abstract
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.
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