Publications

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IEEE Standard 1547-2003 [1] conformance of several interconnected microinverters was performed by Sandia National Laboratories (SNL) to determine if there were emergent adverse behaviors of co-located aggregated distributed energy resources. Experiments demonstrated the certification tests could be expanded for multi- manufacturer microinverter interoperability. Evaluations determined the microinverters' response to abnormal conditions in voltage and frequency, interruption in grid service, and cumulative power quality. No issues were identified to be caused by the interconnection of multiple devices.

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Arc faults are a significant reliability and safety concern for photovoltaic (PV) systems and can cause intermittent operation, system failure, electrical shock hazard, and even fire. Further, arc faults in deployed systems are seemingly random and challenging to faithfully create experimentally in the laboratory, which makes the study of arc fault signature detection difficult. While it may seem trivial to simply record arcing signatures from real-world system, an obstacle in capturing these arc signals is that arc faults in the PV systems do not happen predictably, and depending on the location of the sensors relative to the arc location, may contribute a negligible portion to the magnitude of the sensed current or voltage waveform. The high-frequency content of the arc requires fast sampling, long memory, and fast processing to acquire, store, and analyze the waveforms; this adds substantial balance-of-system cost when considering widespread deployment of arc fault detectors in PV applications. In this paper, we study the performance of the fast Fourier transform arc detection method compared to the wavelet decomposition method by using synthetic waveforms. These waveforms are created by combining measured waveforms of normal background noise from inverters in DC PV arrays along with waveforms of arcing events. Using this technique allows the ratio of amplitudes are varied. Combining these separate waveforms in various amplitude proportions enables creation of test signals for the study of detection algorithm efficacy. It will be shown that the wavelet transformation technique produce more easily recognized detection results and can perform this detection using a much lower sampling rate than what is required for the fast Fourier transform

Many methods have been proposed to detect arc faults within photovoltaic systems. However, because of the dearth of data surrounding arcs that actually occur in commercial or residential PV systems, a sound method is necessary to systematically check for the effectiveness of algorithms claiming the ability to detect PV arc faults. This method should include data representing actual background PV system noise and seek to quantify the limits of the detection capability for the algorithms of interest.

While arc-faults are rare in photovoltaic installations, more than a dozen documented arc-faults have led to fires and resulted in significant damage to the PV system and surrounding structures. In the United States, National Electrical Code® (NEC) 690.11 requires a listed arc fault protection device on new PV systems. In order to list new arc-fault circuit interrupters (AFCIs), Underwriters Laboratories created the certification outline of investigation UL 1699B. The outline only requires AFCI devices to be tested at arc powers between 300-900 W; however, arcs of much less power are capable of creating fires in PV systems. In this work we investigate the characteristics of low power (100-300 W) arc-faults to determine the potential for fires, appropriate AFCI trip times, and the characteristics of the pyrolyzation process. This analysis was performed with experimental tests of arc-faults in close proximity to three polymer materials common in PV systems, e.g., polycarbonate, PET, and nylon 6,6. Two polymer geometries were tested to vary the presence of oxygen in the DC arc plasma. The samples were also exposed to arcs generated with different material geometries, arc power levels, and discharge times to identify ignition times. To better understand the burn characteristics of different polymers in PV systems, thermal decomposition of the sheath materials was performed using infrared spectra analysis. Overall a trip time of less than 2 seconds is recommended for the suppression of fire ignition during arc-fault events.

Many photovoltaic (PV) direct current (DC) arc-fault detectors use the frequency content of the PV system to detect arcs. The spectral content is influenced by the duration and power of the arc, surrounding insulation material geometry and chemistry, and electrode geometry. A parametric analysis was conducted in order to inform the Underwriters Laboratories (UL) 1699B ('Photovoltaic DC Arc-Fault Circuit Protection') Standards Technical Panel (STP) of improvements to arc-fault generation methods in the certification standard. These recommendations are designed to reduce the complexity of the experimental setup, improve testing repeatability, and quantify the uncertainty of the arc-fault radio frequency (RF) noise generated by different PV arcs in the field. In this investigation, we (a) discuss the differences in establishing and sustaining arc-faults for a number of different test configurations and (b) compare the variability in arc-fault spectral content for each respective test, and analyze the evolution of the RF signature over the duration of the fault; with the ultimate goal of determining the most repeatable, 'worst case' tests for adoption by UL.

This work investigates balance of systems (BOS) connector reliability from the perspective of arc fault risk. Accelerated tests were performed on connectors for future development of a reliability model. Thousands of hours of damp heat and atmospheric corrosion tests found BOS connectors to be resilient to corrosion-related degradation. A procedure was also developed to evaluate new and aged connectors for arc fault risk. The measurements show that arc fault risk is dependent on a combination of materials composition as well as design geometry. Thermal measurements as well as optical emission spectroscopy were also performed to further characterize the arc plasma. Together, the degradation model, arc fault risk assessment technique, and characterization methods can provide operators of photovoltaic installations information necessary to develop a data-driven plan for BOS connector maintenance as well as identify opportunities for arc fault prognostics.

Data from of a highly instrumented residential feeder in Ota City, Japan was used to determine 1 second load variability for the aggregation of 50, 100, 250, and 500 homes. The load variability is categorized by binning the data into seasons, weekdays vs. weekends, and time of day to create artificial sub-15-minute variability estimates for modeling dynamic load profiles. An autoregressive, AR(1) function along with a high pass filter was used to simulate the high resolution variability. The simulated data were validated against the original 1-second measured data.

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