Detection and Localization of Damaged Photovoltaic Cells and Modules Using Spread Spectrum Time Domain Reflectometry

Saleh, Mashad Uddin; Deline, Chris; Benoit, Evan; Kingston, Samuel; Harley, Joel B.; Furse, Cynthia; Scarpulla, Michael A.

doi:10.1109/jphotov.2020.3030185

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…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: Sstdr Applicationsmentioning

confidence: 99%

A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…We will use the digital twin and an algorithm based on the peaks seen in the responses to determine the location of disconnections within this system. Other methods to do this include identifying the point at which the baselined X i disconnection data diverged from the zero line data with approximately a 1% change [42], but we have chosen this method for its computational simplicity. Before showing our simulations, it is useful to show the experimental SSTDR responses of the open circuit disconnections and then explain how one would interpret the data to locate each disconnection from experimental data.…”

Section: Validation For Simulation Of Partial Disconnectsmentioning

confidence: 99%

Spread Spectrum Time Domain Reflectometry (Sstdr) Digital Twin Simulation of Photovoltaic Systems for Fault Detection and Location

Kingston¹,

Flamme²,

Saleh³

et al. 2021

PIER B

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Utilizing spread spectrum time domain reflectometry (SSTDR) to detect, locate, and characterize faults in photovoltaic (PV) systems is examined in this paper. We present a method to obtain the model parameters that are needed to produce digital twin SSTDR responses for PV systems. The digital twin SSTDR responses could be used to predict faults within the PV systems. The model parameters are the reflection and transmission coefficients at each impedance discontinuity in the PV system along with the propagation coefficients across each PV cable segment. We obtain model parameter by applying inverse modeling techniques to experimental SSTDR data associated with PV systems. Our model parameters can be used in any digital twin simulation method for modeling reflectometry in frequency-dependent and complex loads. For validation, we used the model parameters in a graph network simulation engine and adapted it to be used for SSTDR digital twin simulations in PV systems. We produced simulations for 0 to 10 PV modules connected in series. We also simulated SSTDR responses for open circuit disconnections in a PV setup containing 10 PV modules in series. Results show that all but one simulated disconnect locations match experimental disconnection locations of the same setup with an error of less than 5%.

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“…In the last decades, many studies focused on PV fault diagnosis have been carried out. The existing methods can be mainly categorized into three types: sensing-based methods [4], [5], [6], electrical characteristics-based methods [7], [8], [9] and machine learning-based (ML) methods [10], [11]. The sensing-based methods and electrical characteristics-based methods rely on manual fault feature extraction and some key parameters need to be determined through specific experiments according to the topology, which reduces the generalization of models and is not suitable for increasingly complex PV stations.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

Liu,

Yang,

Wang

et al. 2022

Preprint

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Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method.

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Detection and Localization of Damaged Photovoltaic Cells and Modules Using Spread Spectrum Time Domain Reflectometry

Cited by 17 publications

References 18 publications

A SSTDR Methodology, Implementations, and Challenges

A SSTDR Methodology, Implementations, and Challenges

Spread Spectrum Time Domain Reflectometry (Sstdr) Digital Twin Simulation of Photovoltaic Systems for Fault Detection and Location

Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

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