Quantifying the Window of Uncertainty for SSTDR Measurements of a Photovoltaic System

Benoit, Evan; Mismash, Jack; Kingston, Samuel; Edun, Ayobami; Ellis, Hunter; LaFlamme, Cody; Scarpulla, Michael A.; Harley, Joel B.; Furse, Cynthia

doi:10.1109/jsen.2021.3059412

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…If these normal impedance changes are larger than the impedance changes from faults, they will mask the faults and make them invisible [ 7 ]. This variability has been measured for aircraft cable fault location [ 7 , 114 ] and PV systems [ 115 ]. Averaging multiple SSTDR signatures is particularly helpful when there are random impedance variations in the system.…”

Section: Sstdr Applicationsmentioning

confidence: 99%

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: Sstdr Applicationsmentioning

confidence: 99%

A SSTDR Methodology, Implementations, and Challenges

Kingston

Benoit

Edun

et al. 2021

Sensors

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“…Although the expected minimum valued trough location of the X 4 data has an 18.5% error, we note that the second least minimum valued trough, which we denote as X ′ location is likely not because of differences in the PV modules. In [43], the variability in SSTDR data was quantified for various parameters, e.g., switching out PV modules. It was found that the greatest change was in the reflection coefficient amplitude not in peak location.…”

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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“…Innovative approaches, such as 2D CNN for fault classification under harsh conditions, 15 and the application of the Teager Kaiser Energy Operator for fault detection, have demonstrated potential but also face challenges in practical application and scalability. [16][17][18] Liu et al 19 introduce a novel clustering method based on dilation and erosion theory, with enhanced fault diagnosis in PV arrays without the need for predetermining fault types. Research efforts have often been fragmented, focusing on specific fault types through methodologies, like, fuzzy logic, 20 neural networks, [21][22][23][24][25] and machine learning 26 leaving a void for a comprehensive fault detection and localization solution.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the reliance on deep learning models necessitates significant computational resources, which might not be readily available in all contexts. Innovative approaches, such as 2D CNN for fault classification under harsh conditions, 15 and the application of the Teager Kaiser Energy Operator for fault detection, have demonstrated potential but also face challenges in practical application and scalability 16–18 . Liu et al 19 introduce a novel clustering method based on dilation and erosion theory, with enhanced fault diagnosis in PV arrays without the need for predetermining fault types.…”

Section: Introductionmentioning

confidence: 99%

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

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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.

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Quantifying the Window of Uncertainty for SSTDR Measurements of a Photovoltaic System

Cited by 8 publications

References 20 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

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