Fault Diagnosis for Electrical Systems and Power Networks: A Review

In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or o...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 21; no. 2; pp. 888 - 906
Main Authors: Furse, Cynthia M., Kafal, Moussa, Razzaghi, Reza, Shin, Yong-June
Format: Journal Article
Language:English
Published: New York IEEE 15.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Computer Science
Current measurement
diagnosis
Electric power grids
Electricity distribution
Electromagnetism
Engineering
Engineering Sciences
fault
Fault detection
Fault diagnosis
Fault location
fault tolerance
Frequency domain analysis
Genetic algorithms
High frequencies
Impedance
Instruments & Instrumentation
inverse problems
Inverse scattering
Machine learning
Methodology
Neural networks
Optimization
Particle swarm optimization
Phasors
Physics
power networks
R&D
Reflectometry
Research & development
Sensors
Signal and Image Processing
Signatures
Spread spectrum
State-of-the-art reviews
Statistics
Test methods
Time domain analysis
Transmission line measurements
transmission lines
Traveling waves
Voltage measurement
Wiring
branched networks
fault diagnosis
backtracking search optimization
Automated analysis technique
teaching–learning-based optimization
fault detection
electrical wiring interconnection systems (EWIS)
orthogonal multi-tone reflectometry (OMTDR)
noise domain reflectometry (NDR)
time domain analysis
wiring
neural networks (NN)
sequence time domain reflectometry (STDR)
defect
multicarrier reflectometry (MCR)
spread spectrum time domain reflectometry (SSTDR)
inverse problems
transferometry
Diagnosis
reflectometry
cable
binary time domain reflectometry (BTDR)
fault tolerance
data processing
fault location
time-frequency domain reflectometry (TFDR)
inverse scattering
fault
frequency domain reflectometry (FDR)
electromagnetic time-reversal (TR)
particle swarm optimization
transmission lines
frequency domain analysis
power networks
reflectometry method
soft fault
small impedance changes
time domain reflectometry (TDR)
genetic algorithm (GA)
matched-pulse (MP)
chaos time domain reflectometry (CTDR)
statistical analysis
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching-learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
EE0008169
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2987321