Search

In this article, we explore the possibility of using spread spectrum time domain reflectometry (SSTDR) for detecting disconnections in a large-scale photovoltaic (PV) array. We discuss the importance, role, and trade-offs of SSTDR resolution, frequency, and attenuation in detecting disconnects in the system. Our results show that if the proper system parameters are chosen, disconnections can be detected in a 1-kV system consisting of twenty-six 60-cell PV panels and located within 1.52 m for the first 22 modules.

Spread spectrum time domain reflectometry (SSTDR) is a non-intrusive method for electrical fault detection and localization that enables continuous monitoring of live electrical systems. Electrical faults create changes in impedance that create subsequent changes in the SSTDR reflection response. These changes in reflection response can be detected only if the changes are outside the window of uncertainty of the SSTDR measurement. In this paper, we establish a method of determining this window of uncertainty and the associated minimum-detectable change in impedance for SSTDR measurements. We demonstrate this for a photovoltaic (PV) systems, although the methods could be similarly applied to other applications. We assess the variability in SSTDR measurements caused by changes in the PV system that are representative of normal maintenance actions such as disconnecting/reconnecting a connector and completely breaking-down/setting-up the entire system. We evaluate how this variability translates to a minimum-detectable change in impedance and how that relates to common faults in PV systems (arc and ground faults, shading, damaged cells, and aging). We also describe methods of increasing SSTDR fidelity to accurately extract minor changes in impedance and therefore, detect small-magnitude electrical faults.

In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, which includes both a misfit function and stabilizer function, is created. The misfit function is a least squares estimate of how close the model data matches observed data. The stabilizer function prevents the steepest descent algorithm from becoming unstable and diverging. Steepest descent iteratively identifies the model parameters that minimize the parametric function. We validate the algorithm by correctly identifying the model parameters (capacitance and resistance) associated with simulated SSTDR data, with added 3 dB white Gaussian noise. With the stabilizer function, the steepest descent algorithm estimates of the model parameters are bounded within a specified range. The errors for capacitance (220pF to 820pF) and resistance (50 Ω to 270 Ω) are < 10%, corresponding to a complex impedance magnitude |R +1/jωC| of 53 Ω to 510 Ω.

This article explains the use of supervised and unsupervised dictionary learning approaches on spread spectrum time domain (SSTDR) data to detect and locate disconnections in a PV array consisting of five panels. The aim is to decompose an SSTDR reflection signature into different components where each component has a physical interpretation, such as noise, environmental effects, and faults. In the unsupervised dictionary learning approach, the decomposed components are inspected to detect and localize faults. The maximum difference between actual and predicted location of the fault is 0.44 m on a system with five panels connected to an SSTDR box with a leader cable of 59.13 m and total length of 67.36 m including the effective length of the panels. In the supervised dictionary learning approach, the dictionary components are used to classify the SSTDR data to their respective fault types. Our results show a 97% accuracy using the supervised learning approach.

Spread spectrum time domain reflectometry (SSTDR) has been traditionally used to detect hard faults (open and short circuit faults) in transmission lines. Prior work has focused on loads at the end of the line with little research on impedances from circuit elements located in the middle of the line (i.e., not at the load) or on only one wire of the line. In this work we consider cases of transmission lines with different impedances on each wire. We refer to lines with the same impedance on both wires (positive and negative) as symmetric. Lines with different impedances on each wire are asymmetric. For highly localized impedances (approximately infinitesimal in length, i.e. with a length significantly smaller than the wavelength of the incident signal), the reflections and their effects on the propagating wave become difficult to describe with traditional transmission line theory. We provide analytical expressions for reflection coefficients for symmetric and asymmetric transmission lines and show that these formulae describe experimental measurements of capacitors and resistors to about 99% accuracy for the magnitudes and 75% for the phases.

The operating efficiency of photovoltaic (PV) plants can be improved if damaged or degraded modules can be detected and identified. Currently, string-level power electronics can detect problems with modules or cabling but not locate them, which would facilitate addressing these issues. Here, we investigate the ability of spread spectrum time domain reflectometry (SSTDR) to both detect and locate/identify damaged cells and modules within a series-connected PV string. We tested the ability of SSTDR to detect and locate single-cell mini-modules and full-sized PV modules, which were intentionally damaged by impacts with a hammer (breaking the glass and damaging the silicon below) or by cutting through some or all busbars. Damage to the glass and silicon of cells was detected and located within a small string of minimodules. Busbar damage was detectable only if an open was created by cutting through all intercell busbars. Physical impact damage to the glass and silicon of a full-sized PV module could be detected, but further development of signal processing is needed to achieve localization of such damaged modules within a string.

In this paper, we present a method for estimating capacitances with SSTDR and a dictionary matching algorithm. We simulate a dictionary of simulated SSTDR reflections for a range of capacitances, based on parameters of the transmitted SSTDR signal, the SSTDR signal generator, and the transmission line. The measured SSTDR reflection data is compared with each simulated reflection through cross-correlation, and the estimated capacitance is found from the highest correlation. We validate this method with experiments on capacitors ranging from 1pF to 450pF using a 12 MHz SSTDR. The dictionary matching algorithm estimated the capacitor values within 4% to 20% in the capacitance range of 100pF to 400pF.

Spread spectrum time domain reflectometry (SSTDR) is a broadband electrical reflectometry technique that has been used to detect and locate faults on live electrical systems, including photovoltaic systems. In this article, we evaluate the detectability and localizability from both existing literature and our own measurements using SSTDR of open-circuit faults, connection faults, short-circuit faults, ground faults, arc faults, shading faults, bypass diode faults, and accelerated degradation faults in PV cells and mini-modules. Reflection magnitudes for these faults are compared. Preliminary data on buried and grounded PV cable along with arc fault detection are presented.

This article describes the novel use of spread-spectrum time-domain reflectometry (SSTDR) for detecting and locating disconnection faults in photovoltaic (PV) power plants. We measure strings of cells and full-sized modules to understand how disconnections affect the reflectometry signature. PV modules correspond to reactive loads and disconnections in a PV string and cause distinguishable and measurable changes in SSTDR responses. Disconnection faults are manually introduced and correctly identified and localized applying the proposed technique. The SSTDR technique can detect and locate disconnects through at least eight 36-cell modules. Using the new technique described in this article, we can both detect and locate the disconnections within a 1-ft resolution, on average.

Reflectometry, which is commonly used for locating faults on electrical wires, produces sampled time domain signatures with peaks that are often missed due to this sampling. Resultant errors in these sampled peaks translate to errors in calculating the impedance and location of the fault. Typical signal processing methods to improve the accuracy of these sampled peaks have complexity on the order of O(N2). For embedded fault location applications, algorithms with lower complexity are desired. In this article, we introduce three algorithms for improving the accuracy of the peak with a complexity of O(N). We evaluate these algorithms on the practical case of calculating the velocity of propagation and the characteristic impedance of a photovoltaic (PV) cable using spread spectrum time-domain reflectometry.

We present a framework for analyzing electromagnetic signal propagation through piecewise-defined transmission lines with arbitrary, series-connected impedances. While the formulation is general and scalable, we apply it here to propagation through a photovoltaic module with cables on either side acting, with a home run cable, as a section of an inhomogeneous transmission line. Understanding propagation through this unit of a series-connected string of photovoltaic modules is necessary to enable the use of time-domain reflectometry techniques for monitoring the status of individual components in series-connected strings within large photovoltaic arrays.

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.

Spread spectrum time domain reflectometry (SSTDR) can be used to measure complex impedances in live electrical systems by evaluating the shape and magnitude of the reflected signature. A range of capacitors or inductors can be identified, but above and below this range, they cannot be distinguished from open or short circuits. This range depends on the frequency band of the reflectometry system and characteristic impedance of the transmission line. This can be used to characterize changes in complex impedance for the purpose of fault detection in complex loads, such as PV panels and systems.

This paper describes new algorithms for spread spectrum time domain reflectometry (SSTDR) for detecting and locating faults in photovoltaic (PV) panels. Specifically, we present a new method for identifying the impedance of multiple loads (such as PV panels) on a single transmission line. This method is based on adapting a well-known algorithm, known as orthogonal matching pursuit, to match simulated waveforms with multiple reflections in an SSTDR waveform. We demonstrate that our method successfully extracts waveform data from the SSTDR experiments and correctly estimates load impedances corresponding to reflections in this waveform data. We experimentally demonstrate that our method correctly estimates the capacitance values of various capacitive loads on a transmission line with an error as low as4%.

Spread spectrum time domain reflectometry (SSTDR) has previously been used for detection and location of intermittent faults on live electrical wiring. These intermittent faults can be open circuits, short circuits, or resistive changes, all of which preserve the original shape of the SSTDR correlated waveform. But things are very different when SSTDR encounters a complex impedance discontinuity such as a capacitor or inductor. In this case, the reflection is a function of frequency, changing the shape of the SSTDR signature. In this paper, we will show the SSTDR response to single capacitors and inductors. We will also explore how SSTDR responds to arrays of PV panels (which are capacitive) connected by wires. We will show both simulations and measurements. In some configurations, it is relatively easy to see faults, although algorithms are still under development. In other configurations, little change occurs, which makes it very difficult to create a system for testing for these faults.

We have prepared Ni–Fe–B nanotube arrays of length 20 $\mu \text{m}$ and outer diameter 400 nm in polycarbonate membranes via electroless deposition using dimethylamine borane as a reducing agent. The alloy composition was varied by controlling the FeSO4 concentration in the deposition solution, and the effect of this variation on the nanostructures and magnetic properties was investigated. We found that the specific magnetization of the nanostructures varies linearly with Fe content, with a maximum value of 84.5 JT $^{\mathrm {-1}}$ kg $^{\mathrm {-1}}$ obtained for Ni62Fe38–B. The Ni–Fe–B nanotubes are magnetically soft with an increase in Fe content, resulting in a reduced coercivity and squareness. Our results also show that vortex mode is the dominant reversal magnetization mechanism in the nanotubes. In this paper, we show that the magnetic properties of Ni–Fe–B nanotubes can be tailored through varying the Fe content.

Nanostructured magnetic materials have found application in the electronic, magnetic data storage, chemical separation and in biological sectors.

The mechanical, ferroelectric, and ferromagnetic properties of materials with dimensions in the nanometer range differ significantly from their bulk properties [1]. Unique magnetic properties can be tailored through compositional and morphological control of the deposited nanostructures [2]. These unique properties significantly increase the potential of magnetic nanostructures as active materials in many modern electronic and magnetic devices such as high density data storage devices, power conversion and energy storage devices. In this study, we synthesize Ni-Fe nanotubes of varying compositions via electroless deposition using both dimethylamine borane (DMAB) and sodium hypophosphite as reducing agents. We investigate the structure and magnetic properties of Ni 100-X Fe X nanotubes with 10 < X < 68.

peer-reviewed Co-B nanotubes were prepared via an electroless deposition method. The morphology, magnetic properties and the magnetization reversal mechanism of the nanotubes were investigated. Deposition was carried out in porous polycarbonate membranes leading to the formation of Co-B nanotubes with an average external diameter of 400 nm and lengths up to 6 μm. Electroless deposition resulted in the formation of alloys with composition Co70B30 and a specific magnetization of 65.6 J T−1kg−1, which is about 40 % of that of pure Co (161 J T−1kg−1). The transversal and vortex modes were identified as the mechanisms responsible for magnetization reversal in the nanotubes. A crossover between the two modes is observed at low angles and the results are in line with current models for switching mechanisms of nanotubes.