Publications

Abstract not provided.

Current-voltage (I-V) curve traces of photovoltaic (PV) systems can provide detailed information for diagnosing fault conditions. The present work implemented an in situ, automatic I-V curve tracer system coupled with Support Vector Machine and a Gaussian Process algorithms to classify and estimate abnormal and normal PV performance. The approach successfully identified normal and fault conditions. In addition, the Gaussian Process regression algorithm was used to estimate ideal I-V curves based on a given irradiance and temperature condition. The estimation results were then used to calculate the lost power due to the fault condition.

Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

We describe and compare two methods for modeling irradiance on the back surface of rack-mounted bifacial PV modules: view factor models and ray-tracing simulations. For each method we formulate one or more models and compare each model with irradiance measurements and short circuit current for a bifacial module mounted a fixed tilt rack with three other similarly sized modules. Our analysis illustrates the computational requirements of the different methods and provides insight into their practical applications. We find a level of consistency among the models which indicates that consistent models may be obtained by parameter calibrations.

To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. The low cost and ease of use of the SVD will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.

Abstract not provided.

Abstract not provided.

A 9.6 kW test array of Prism bifacial modules and reference monofacial modules installed in February 2016 at the New Mexico Regional Test Center has produced six months of performance data. The data reveal that the Prism modules are out-performing the monofacial modules, with bifacial gains in energy over the six-month period ranging from 18% to 136%, depending on the orientation and ground albedo. These measured bifacial gains were found to be in good agreement with modeled bifacial gains using equations previously published by Prism. The most dramatic increase in performance was seen among the vertically tilted, west-facing modules, where the bifacial modules produced more than double the energy of monofacial modules and more energy than monofacial modules at any orientation. Because peak energy generation (mid-morning and mid-afternoon) for these bifacial modules may best match load on the electric grid, the west-facing orientation may be more economically desirable than traditional south-facing module orientations (which peak at solar noon).

The Regional Test Centers are a group of several sites around the US for testing photovoltaic systems and components related to photovoltaic systems. The RTCs are managed by Sandia National Laboratories. The data collected by the RTCs must be transmitted to Sandia for storage, analysis, and reporting. This document describes the methods that transfer the data between remote sites and Sandia as well as data movement within Sandia’s network. The methods described are in force as of September, 2016.

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.

PV project investments need comprehensive plant monitoring data in order to validate performance and to fulfil expectations. Algorithms from PV-LIB and Loss Factors Model are being combined to quantify their prediction improvements at Gantner Instruments' Outdoor Test facility at Tempe AZ on multiple Tier 1 technologies. The validation of measured vs. predicted long term performance will be demonstrated to quantify the potential of IV scan monitoring. This will give recommendations on what parameters and methods should be used by investors, test labs, and module producers.

Cost effective integration of solar photovoltaic (PV) systems requires increased reliability. This can be achieved with a robust fault detection and diagnostic (FDD) tool that automatically discovers faults. This paper introduces the Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network to perform this task. The present work tested the algorithm on actual and synthetic data to assess its potential for wide spread implementation. The tests were conducted on a PV system located in Albuquerque, New Mexico. The system was composed of 14 modules arranged in a configuration that produced a maximum power of 3.7kW. The LAPART algorithm learned system behavior quickly, and detected module level faults with minimal error.

Accurate photovoltaic system performance monitoring is critical for profitable long-term operation. Irradiance, temperature, power, current and voltage signals contain rapid fluctuations that are not observable by typical monitoring systems. Nevertheless these fluctuations can affect the accuracy of the data that are stored. We closely examine electrical signals in one operating PV system recorded at 2000 samples per second. Rapid fluctuations are analyzed, caused by line-frequency harmonics, anti-islanding detection, MPPT and others. The operation of alternate monitoring systems is simulated using a wide range of sampling intervals, archive intervals and filtering options to assess how these factors influence final data accuracy.

Air mass modifiers are frequently used to represent the effects of solar spectrum on PV module current. Existing PV module performance models assume a single empirical expression, a polynomial in air mass, for all locations and times. In this paper, air mass modifiers are estimated for several modules of different types from IV curves measured with the modules at fixed orientation in three climatically different locations around the United States. Systematic variation is found in the effect of solar spectrum on PV module current that is not well approximated by the standard air mass modifier polynomial.

To address the lack of knowledge of local solar variability, we have developed, deployed, and demonstrated the value of data collected from a low-cost solar variability sensor. While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we list variability sensor requirements, describe testing of various low-cost sensor components, present a validation of an alpha prototype, and show how the variability sensor collected data can be used for grid integration studies. The variability sensor will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic installations and thus will encourage greater penetrations of solar energy.

Sandia National Laboratories (Sandia) manages four of the five PV Regional Test Centers (RTCs). This report reviews accomplishments made by the four Sandia-managed RTCs during FY2015 (October 1, 2014 to September 30, 2015) as well as some programmatic improvements that apply to all five sites. The report is structured by Site first then by Partner within each site followed by the Current and Potential Partner summary table, the New Business Process, and finally the Plan for FY16 and beyond. Since no official SOPO was ever agreed to for FY15, this report does not include reporting on specific milestones and go/no-go decisions.