公开(公告)号：US10852359B2

公开(公告)日：2020-12-01

申请号：US15831558

申请日：2017-12-05

Applicant: The University of Hong Kong

Inventor： Wing Tat Pong ,  Ke Zhu

IPC: G01R31/50 ,  G01R31/08 ,  G01R15/20 ,  H02H1/04 ,  G01R19/165 ,  H02H3/50 ,  G01R31/00

Abstract: A DC-component-based fault classification apparatus and method for a three-phase power distribution cable utilizes the reconstructed three-phase currents by measuring the magnetic field around the cable with an array of magnetic sensors arranged around the cable surface. A magnetic shield houses the magnetic sensors and blocks background magnetic fields. A data acquisition system acquires analog signals from the sensors and a processing system extracts DC components in the analog signals for the phases during the transient period after a fault. The potential DC components are extracted by mathematical morphology. These DC components arise in the faulted phases when a fault occurs since there is a large current change in the inductive power network.

公开(公告)号：US12224585B2

公开(公告)日：2025-02-11

申请号：US17753766

申请日：2019-09-12

Applicant: The University of Hong Kong

Inventor： Wing Tat Pong ,  Ke Zhu ,  Qi Xu

IPC: G01R13/00 ,  G01R19/25 ,  H02J3/00 ,  H02J3/12 ,  H02J13/00

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.