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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.

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.

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.

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.

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.