Publications

Abstract not provided.

While the 2011 National Electrical Codeª (NEC) only requires series arc-fault protection, some arc-fault circuit interrupter (AFCI) manufacturers are designing products to detect and mitigate both series and parallel arc-faults. Sandia National Laboratories (SNL) has extensively investigated the electrical differences of series and parallel arc-faults and has offered possible classification and mitigation solutions. As part of this effort, Sandia National Laboratories has collaborated with MidNite Solar to create and test a 24-string combiner box with an AFCI which detects, differentiates, and de-energizes series and parallel arc-faults. In the case of the MidNite AFCI prototype, series arc-faults are mitigated by opening the PV strings, whereas parallel arc-faults are mitigated by shorting the array. A range of different experimental series and parallel arc-fault tests with the MidNite combiner box were performed at the Distributed Energy Technologies Laboratory (DETL) at SNL in Albuquerque, NM. In all the tests, the prototype de-energized the arc-faults in the time period required by the arc-fault circuit interrupt testing standard, UL 1699B. The experimental tests confirm series and parallel arc-faults can be successfully mitigated with a combiner box-integrated solution.

Ground faults in photovoltaic (PV) systems pose a fire and shock hazard. To mitigate these risks, AC-isolated, DC grounded PV systems in the United States use Ground Fault Protection Devices (GFPDs), e.g., fuses, to de-energize the PV system when there is a ground fault. Recently the effectiveness of these protection devices has come under question because multiple fires have started when ground faults went undetected. In order to understand the limitations of fuse-based ground fault protection in PV systems, analytical and numerical simulations of different ground faults were performed.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Arcing in PV systems has caused multiple residential and commercial rooftop fires. The National Electrical Code® (NEC) added section 690.11 to mitigate this danger by requiring arc-fault circuit interrupters (AFCI). Currently, the requirement is only for series arc-faults, but to fully protect PV installations from arc-fault-generated fires, parallel arc-faults must also be mitigated effectively. In order to de-energize a parallel arc-fault without module-level disconnects, the type of arc-fault must be identified so that proper action can be taken (e.g., opening the array for a series arc-fault and shorting for a parallel arc-fault). In this work, we investigate the electrical behavior of the PV system during series and parallel arc-faults to (a) understand the arcing power available from different faults, (b) identify electrical characteristics that differentiate the two fault types, and (c) determine the location of the fault based on current or voltage of the faulted array. This information can be used to improve arc-fault detector speed and functionality. © 2013 IEEE.

The 2011 National Electrical Code® Article 690.11 requires photovoltaic systems on or penetrating a building to include a DC arc-fault protection device. In order to satisfy this requirement, new Arc-Fault Detectors (AFDs) are being developed by multiple manufacturers including Sensata Technologies. Arc-fault detection algorithms often utilize the AC noise on the PV string to determine when arcing conditions exist in the DC system. In order to accelerate the development and testing of Sensata Technologies' arc-fault detection algorithm, Sandia National Laboratories (SNL) provided a number of data sets. These prerecorded 10 MHz baseline and arc-fault data sets included different inverter and arc-fault noise signatures. Sensata Technologies created a data evaluation method focused on regeneration of the prerecorded arcing and baseline test data with an arbitrary function generator, thereby reducing AFD development time. © 2012 IEEE.

A novel model-based prognostics and health management (PHM) system has been designed to monitor the health of a photovoltaic (PV) system, measure degradation, and indicate maintenance schedules. Current state-of-the-art PV monitoring systems require module and array topology details or extensive modeling of the PV system. We present a method using an artificial neural network (ANN) which eliminates the need for a priori information by teaching the algorithm "good" performance behavior based on the initial performance of the array. The PHM algorithm was tested on two PV systems under test at the Outdoor Test Facility (OTF) at the National Renewable Energy Laboratory (NREL). The PHM algorithm was trained using two months of AC power production. The model then predicted the output power of the system using irradiance, wind, and temperature data. Based on the deviation in measured AC power from the AC power predicted by the trained ANN model, system outages and other faults causing a reduction in power were detected. Had these been commercial installations, rather than research installations, an alert for maintenance could have been initiated. Further use of the PHM system may be able to indicate degradation, detect module or inverter failures, or detect excessive soiling. © 2012 IEEE.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.