An Overview of Spread Spectrum Time Domain Reflectometry Responses to Photovoltaic Faults

Saleh, Mashad Uddin; Deline, Chris; Benoit, Evan; Kingston, Samuel; Edun, Ayobami; Jayakumar, Naveen Kumar Tumkur; Harley, Joel B.; Furse, Cynthia; Scarpulla, Michael A.

doi:10.1109/jphotov.2020.2972356

Cited by 32 publications

(7 citation statements)

References 35 publications

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“…Reflectometry is a nondestructive technique to remotely detect, locate, and characterize useful information in physical systems. It has been used extensively in electrical systems to locate electrical “opens” or “shorts” [ 1 , 2 , 3 , 4 , 5 , 6 ], locate cable insulation break down [ 7 ], detect electrical system degradation [ 8 , 9 , 10 , 11 ], locate ground and arc faults [ 11 , 12 , 13 , 14 ], locate damaged components in photovoltaic (PV) systems [ 12 , 15 , 16 , 17 , 18 ], remotely measure complex impedance [ 17 , 19 , 20 , 21 , 22 ], locate intermittent faults that only occur during normal operation [ 3 , 4 , 8 , 23 , 24 , 25 , 26 ], and locate faults in three phase systems [ 27 ]. In material science, reflectometry has been used to characterize nanostructures, optical fiber networks, waveguides, and dielectrics [ 28 , 29 , 30 , 31 ].…”

Section: Reflectometrymentioning

confidence: 99%

“…It has been used in airplane cabling where it has been able to detect intermittent faults lasting a few milliseconds [ 47 ]. Application has also been seen in power systems [ 27 , 57 , 58 ], PV systems [ 11 , 12 , 13 , 15 , 16 , 18 , 59 , 60 ], undersea cabling [ 61 ], and railroad systems [ 49 , 62 , 63 ]. It has been proposed that SSTDR could be used in battery health monitoring [ 64 ] and cable degradation monitoring [ 65 ].…”

Section: Reflectometrymentioning

confidence: 99%

“…An overview of using SSTDR with PV systems was presented in [ 11 ]. SSTDR has been used on live PV arrays to find the position of wiring faults [ 105 ], ground faults [ 13 , 106 ], arc faults [ 12 ], disconnection faults [ 107 ], accelerated degradation [ 11 ], shading faults and broken panels [ 60 , 108 ]. The feasibility of using SSTDR to detect and locate faults for PV is summarized in Table 1 .…”

Section: Sstdr Applicationsmentioning

confidence: 99%

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A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

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Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

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Section: Reflectometrymentioning

confidence: 99%

Section: Reflectometrymentioning

confidence: 99%

Section: Sstdr Applicationsmentioning

confidence: 99%

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A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

Self Cite

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“…However, due to the cables being buried underground in complex and damp environments, minor defects are prone to occur. If not repaired in a timely manner, these early minor defects may accumulate into permanent faults, leading to widespread power outages and economic losses [1][2][3][4] . To reduce the losses caused by cable faults, extensive research has been conducted on power cable fault diagnosis techniques both domestically and internationally.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of power cables based on wavelet transform and CNN-informer

Li,

Wang,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Accurate identification of early-stage cable faults is a necessary prerequisite for timely elimination of fault hazards. This article proposes a method for power cable fault diagnosis using wavelet transform and CNN-Informer, which can accurately identify early-stage cable faults from overcurrent signals caused by constant impedance faults, capacitor switching, and excitation inrush currents. The method extracts features from the overcurrent signals using wavelet transform and then constructs a CNN-Informer network fusion model. The model is trained by adjusting the network parameters to establish the mapping relationship between input features and class encoding. Simulation results show that the proposed method can effectively classify overcurrent signals and accurately identify early-stage cable faults, demonstrating high practical value in engineering applications. Furthermore, comparative simulation experiments with similar methods conclude that the proposed method in this article achieves higher accuracy and precision.

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“…The TDR method determines the location of cable faults by transmitting a pulse signal at the beginning or end of the faulty cable and calculating the time difference between the incident and reflected signals [ 5 ]. The authors of [ 6 , 7 ], based on the TDR method, emitted compressed pulses to detect the status of cable joints, but the waveforms obtained could only determine open and short circuit faults, without high resistance and low resistance faults. Additionally, the accuracy of the cable defect location was low, due to the low content of high−frequency components of the pulse signal.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

Wan

Yuan

et al. 2023

Sensors

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To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

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An Overview of Spread Spectrum Time Domain Reflectometry Responses to Photovoltaic Faults

Cited by 32 publications

References 35 publications

A SSTDR Methodology, Implementations, and Challenges

A SSTDR Methodology, Implementations, and Challenges

Fault diagnosis of power cables based on wavelet transform and CNN-informer

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

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