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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.

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.

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.

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.

Spread spectrum time domain reflectometry (SSTDR) has proven to be effective in the detection and localization of intermittent faults on live electrical wiring. The location, shape, and magnitude of the peaks from the SSTDR signal give information about the location and type of wiring fault. To locate these faults, baseline subtraction is typically used. Unfortunately, varying weather conditions alter the SSTDR signal significantly and make baseline subtraction methods unreliable. Specifically, moisture from rain changes the relative permittivity of the cable and consequently leads to a change in the velocity of propagation (VOP) of the SSTDR signal. This change of VOP stretches the SSTDR signal and therefore shifts the peaks out of place, leading to inaccurate fault localization. Figure 1 (below) shows the described stretching effect rain has on SSTDR signals.

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.