Spread Spectrum Time Domain Reflectometry Research Articles

Reducing the Effects of Rain and Moisture on Spread Spectrum Time-Domain Reflectometry Monitoring of Photovoltaics

Reducing the Effects of Rain and Moisture on Spread Spectrum Time-Domain Reflectometry Monitoring of Photovoltaics

A Novel Method Based on Spread-Spectrum Time-Domain Reflectometry for Improving the Distance of Cable Fault Detection

Spread-spectrum time-domain reflectometry (SSTDR) is used to detect cable faults online. In this study, the signal model of SSTDR cable fault detection was established, and two factors, namely the incident leakage signal and the impedance mismatch between the device and the cable, that affect fault detection performance were analyzed. In this study, an improved method of SSTDR cable fault detection was proposed by combining the transmitter–receiver isolation and trapper. Calibration was performed to obtain the optimal parameters of the circuit. The proposed method can reduce the signal energy leakage from the transmitter to the receiver circuit and suppress the impedance mismatch between the device and the cable. The parameter calibration process of transmitter–receiver isolation and trapper, and the influence of various parameters on the performance of cable fault detection were detailed experimentally. The experiments revealed that the improved SSTDR fault detection can detect faults at various distances. At frequencies of 20, 10, and 5 MHz, the fault detection distance reached 68, 110, and 210 m, respectively. Test results revealed the effectiveness of the proposed method.

Application of Augmented Spread Spectrum Time Domain Reflectometry for Detection and Localization of Soft Faults on a Coaxial Cable

Aviation cables are an essential part of aircraft, which transmits signals and power. Aviation cable faults often occur due to drastic changes in temperature and humidity and the effects of vibration. Obvious cable faults, such as open circuits and short circuits, can be detected during maintenance. However, some soft faults like the loose connection of the connector, damage of the insulation layer, and improper cable bending radius cannot be detected. Spread spectrum time domain reflectometry (SSTDR) is a diagnostic method for cable faults; however, the amplitude of the reflected signal is low when the soft fault occurs. SSTDR is only sensitive to apparent faults, but it is insufficient to detect soft faults. This article proposes an aviation cable fault detection and location method, augmented spread spectrum time domain reflectometry (ASSTDR), to detect soft faults effectively. First, the SSTDR results are decomposed into some intrinsic mode functions (IMFs) using the improved variational mode decomposition and selects the IMF with the highest kurtosis to analyze. Second, short-time Fourier transform (STFT) is applied to deal with the selected IMF to enhance the amplitude characteristics. Then, reassign spectrogram is computed to improve the resolution of STFT time-frequency distribution images to locate the location of soft faults. Finally, the effectiveness of ASSTDR is verified by experiments with different fault types and fault degrees. The comparative experiments demonstrate that the method can accurately detect soft faults.

Anomaly Detection of Disconnects Using SSTDR and Variational Autoencoders

This article utilizes variational autoencoder (VAE) and spread spectrum time domain reflectometry (SSTDR) to detect, isolate, and characterize anomalous data (or faults) in a photovoltaic (PV) array. The goal is to learn the distribution of non-faulty input signals, inspect the reconstruction error of test signals, flag anomalies, and then locate or characterize the anomalous data using a predicted baseline rather than a fixed baseline that might be too rigid. The use of VAE handles imbalanced data better than other methods used for classification of PV faults because of its unsupervised nature. We consider only disconnects in this work, and our results show an overall accuracy of 96% for detecting true negatives (non-faulty data), a 99% true positive rate of detecting anomalies, 0.997 area under the ROC curve, 0.99 area under the precision-recall curve, and a maximum percentage absolute relative error of 0.40% in locating the faults on a 5-panel setup with a 59.13 m leader cable.

Quantifying the Environmental Sensitivity of SSTDR Signals for Monitoring PV Strings

Current spread spectrum time-domain reflectometry (SSTDR) fault detection methods in photovoltaics compare measurements with a fault-free baselin.Yet, environmental factors, such as illuminance, temperature, and humidity, affect these signals and can negatively affect our ability to detect and locate faults. This article explains and quantifies the effects of environmental factors on SSTDR measurements. We demonstrate that illuminance, temperature, and humidity each significantly affect reflections from photovoltaic panels, which require the use of up to 240 baselines to prevent environmental variation from obscuring faults. We present a method to determine the number of required baselines in any given climate and motivate future work in baseline prediction.

A SSTDR Methodology, Implementations, and Challenges.

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.

Signals Passing Through Asymmetric Faults in Transmission Lines

Unexpected results have been seen in multiple papers involving spread spectrum time domain reflectometry (SSTDR) measurements on twin lead cables with one line containing a fault. A small portion of the signal is able to transmit past the fault, reflect off the end of the cable, return back through the fault, and be recorded by the SSTDR. This paper explains the physics of why this can happen, and the effect of nearby ground planes. The transmission through the fault involves electric and magnetic fields coupling to nearby conductive objects. This coupling allows for a small amount of signal to move beyond the fault. We use multi-conductor transmission line theory to explain this effect. A finite difference time domain simulation of the multi-conductor system is compared to measured results to demonstrate why this effect occurs, and the effect of the nearby ground.

Detection and Localization of Disconnections in a Large-Scale String of Photovoltaics Using SSTDR

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 Techniques for Measurement of Dielectric Aging on Low Voltage Cables for Nuclear Power Plants

Spread spectrum time domain reflectometry (SSTDR) is used to test dielectric aging of a cable type used in the nuclear power plant industry. A 16 AWG twisted pair shielded cable is thermally aged to the equivalence of 50 years in service, and SSTDR data are collected at 10-year intervals during the aging process. The SSTDR data shows that velocity of propagation and characteristic impedance decrease as the cable ages (increasing the electrical permittivity), making them suitable markers for monitoring aging and demonstrating the ability of SSTDR to determine this aging.

Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion

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.

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

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.

Spread Spectrum Time Domain Reflectometry With Lumped Elements on Asymmetric Transmission Lines

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.

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

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.

Aging Detection and State of Health Estimation of Live Power Semiconductor Devices Using SSTDR Embedded PWM Sequence

Power semiconductor switches undergo degradation due to environmental and electro-thermal stresses resulting in an impedance variation within the device. This impedance variation can be characterized using reflectometry, which is a well-known technique in electromagnetics. A condition monitoring algorithm for power devices in a live power converter using spread spectrum time domain reflectometry (SSTDR) method has been introduced in this article. SSTDR has been successfully used for locating transmission line faults and detecting device degradation in power converters while running at static condition. However, the rapid variation in impedance throughout the entire live converter circuit caused by fast switching makes condition monitoring more challenging while using SSTDR. This article addresses these shortcomings by proposing a novel solution that can be successfully used to identify the aging of the power devices in a live converter. SSTDR test signal was applied at various test nodes in three different circuits: a single-phase H-bridge photovoltaic inverter, a dc–dc buck converter, and a three-phase inverter. The experimental results demonstrate that the SSTDR test data are consistent with the aging of the power devices and do not affect the switching performance of the modulation process even the test signal is applied across the gate-source interface of the power mosfet . This implies that the SSTDR technique can be integrated with the gate driver module, thereby creating a new platform for an intelligent gate-driver architecture that enables real-time health monitoring of power devices while performing features offered by a commercially available driver.

A Model for SSTDR Signal Propagation Through Photovoltaic Strings

The ability of spread spectrum time domain reflectometry (SSTDR) to detect and locate faults in photovoltaic (PV) systems is considered in this article. This article provides a simulation that could be used for studying how faults and other parameters affect the reflectometry response and evaluating fault detection algorithms, for providing comparison simulations in iterative inversion algorithms used to detect and locate faults, or it could potentially be used to replace the measured baseline for these algorithms. The simulation uses an enhanced systematic solution procedure to find the reflection signature of the PV system, and is capable of modeling elements with complex impedance in both series and parallel separated by transmission lines. This article is an expansion of previous work to be capable of simulating systems containing n number of elements (such as PV panels and transmission lines) in series or parallel. The simulation was tested for 20 systems containing up to four resistors, capacitors, and PV cells, and was compared to the measured data.

Online Detection of Aircraft ARINC Bus Cable Fault Based on SSTDR

In order to realize the online detection of ARINC bus cable fault, a method of spread spectrum time-domain reflectometry (SSTDR) is proposed in this article, which can minimize the influence between the ARINC bus signal and incident signal, and meet the requirements of aircraft airworthiness. First, the impedance characteristics and reflection characteristics of the aircraft ARINC bus cables are analyzed so that the online detection model based on SSTDR is constructed. Then, the SSTDR incident signal is designed by studying the relationship between the incident signal parameters and the ARINC bus characteristics. Finally, the proposed method is verified through the online detection platform of ARINC 429/825 bus cable fault, and the experimental signals are analyzed in the time domain and the frequency domain, respectively, which indicates that the ARINC bus signal and the incident signal do not interfere with each other. The experimental results prove that the method of SSTDR is effective and feasible to the online detection of ARINC bus cable fault.

Spread Spectrum Sensing Based on ZCZ Sequences for the Diagnosis of Noisy Wired Networks

Spread spectrum reflectometry has been shown to be an effective method for continuous health monitoring of electrical systems in many different applications. The suitability of Zero Correlation Zone (ZCZ) sequences in Sequence Time Domain Reflectometry (STDR) method has been demonstrated in a previous work. In fact, the ZCZ-STDR method showed excellent potential for simultaneous diagnosis of multiple cables. This paper focuses on evaluating the noise robustness and the effect of branched wire networks on the performance of both STDR and Spread Spectrum Time Domain Reflectometry (SSTDR) methods based on ZCZ sequences.

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

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.

Fault Diagnosis for Electrical Systems and Power Networks: A Review

In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching-learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.

Bond Wire Damage Detection and SOH Estimation of a Dual-Pack IGBT Power Module Using Active Power Cycling and Reflectometry

High thermal and electrical stress, over a period of time tends to deteriorate the health of power electronic switches. Being a key element in any high-power converter systems, power switches, such as insulated-gate bipolar junction transistors (IGBTs) and metal-oxide semiconductor field-effect transistors, are constantly monitored to predict when and how they might fail. A huge fraction of research efforts involves the study of power electronic device reliability and development of novel techniques with higher accuracy in the health estimation of such devices. Until today, no other existing technique can determine the number of lifted bond wires and their locations in a live IGBT module, although this information is extremely helpful to understand the overall state-of-health of an IGBT power module. Through this article, a new method for online condition monitoring of IGBTs and IGBT modules using spread spectrum time-domain reflectometry has been proposed. Unlike traditional methods, this research work concentrates at the gate terminals (low voltage) of the device instead of looking at the collector side. In addition, the RL-equivalent circuit to represent a bond wire has been developed for the device under test and simulated in CST Studio Suite to measure the reflection amplitudes. Experimental results were obtained using a prototype reflectometry hardware, and both the simulation and experimental results have been compared. These results prove that a single measurement is sufficient to predict the failure of the device instead of looking at the traditional precursor parameter (VCEON). With only two sets of measurements, it is possible to locate the aged device inside a module and detect the number of bond wire liftoffs associated with that device.