Postprocessing for Improved Accuracy and Resolution of Spread Spectrum Time-Domain Reflectometry

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

doi:10.1109/lsens.2019.2916636

Cited by 23 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If peak detection algorithms are used, this can result in inaccuracy in both the location and magnitude of the impedance change. Methods to fit or extrapolate these peaks [ 142 ], or the use of a sign eliminator [ 143 ], can significantly improve the results.…”

Section: Sstdr Signal Processing Algorithmsmentioning

confidence: 99%

A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Sstdr Signal Processing Algorithmsmentioning

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

“…The distance ∆d PV+cables is the calculated effective length given the time delay for the incident signal to arrive back to the SSTDR test device and a chosen VoP of the signal. For the VoP, we chose to use the measured VoP [33] through a PV cable of 0.721c where c ≈ 3 × 10 8 m/s (the speed of light in a vacuum). Since we are measuring the effective length and not necessarily the true length, we can use the same VoP for the PV modules and the connecting cables.…”

Section: Propagation Coefficients For In-line Pv Modulesmentioning

confidence: 99%

“…We found Z SSTDR (ω) to be characterized by a resistance of 68 Ω in series with a capacitance of 270 pF. We first found the 68 Ω resistance [32,33] by connecting a potentiometer directly to the SSTDR box and adjusting it whilst taking SSTDR measurements at each step and noting the potentiometer impedance that produced the minimum reflection response (effectively producing a matched load). We found that 68 Ω matched best with the test device impedance.…”

Section: Reflection Coefficient At the Sstdr Test Devicementioning

confidence: 99%

“…2 shows the normalized SSTDR responses for both measurements. We up-sampled the SSTDR response by a factor of 10 for both measurements using spline interpolation [33] and normalized the amplitudes by dividing both data sets by the maximum value in the open circuit data, giving…”

Section: Symbolmentioning

confidence: 99%

“…Without this manual shift in the data, all the maximum magnitude peaks and troughs would be at the same location, making it difficult to distinguish between the respective data sets. Therefore, because of the transmission coefficient, the open circuit response that would normally have a maximum magnitude peak can be flipped up-side-down to be a minimum valued trough, similar to a traditional short circuit load response [33].…”

Section: Validation For Simulation Of Partial Disconnectsmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

View full text Add to dashboard Cite

This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.

show abstract

Postprocessing for Improved Accuracy and Resolution of Spread Spectrum Time-Domain Reflectometry

Cited by 23 publications

References 13 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

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Contact Info

Product

Resources

About