Sandia National Laboratories sent this bulletin at 03/24/2016 06:46 PM MDT
March 2016
Latest News
The Sandia Solar Programs e-newsletter features highlights, key activities, events, and publications. News is also available on Sandia’s solar program website. We welcome your feedback. We hope you enjoy this valuable resource.
Upcoming Events
Sandia
National Laboratories and the Electric Power Research Institute are pleased
to host the 3rd PV Systems Symposium, May 9-11, 2016, on the technical challenges
and opportunities related to solar photovoltaic (PV) systems and technologies. Held
at the Biltmore Hotel and Suites in Santa Clara, California, the symposium’s
three core workshops are PV performance modeling, PV integration into
distribution, and PV system component lifecycle. Although the period for submitting
abstracts has passed, symposium
registrationremains open through Monday, May 9, 2016. Contact: Josh Stein
Research Updates
DOE selects two companies to work with Sandia under the Small Business Vouchers Pilot
Assistant Secretary of Energy David Danielson announced the first-round awardees in DOE/EERE's Small Business Vouchers Pilot (SBV) that pairs national laboratories with eligible small businesses. Sandia’s solar programs were selected to provide technical assistance to two small businesses.
Renewable Power Conversion, Inc. (RPC), was awarded a voucher for its project “Advanced Functionality Testing of RPC PV
Inverter” (Figure 1). Sandia Labs will work with RPC to complete the inverter development for commercial, industrial, and
utility scale applications.
SkySun,
LLC, was awarded a voucher to develop its low cost heliostat (solar
receiver) coordinate arrangement with applications in concentrating solar
power and concentrating PV (Figure 2). Sandia Labs will work with SkySun on its technology to achieve
commercialization.
Figure 1. Renewable Power Conversion, Inc., will receive technical support from Sandia to complete the development of its Macro-Micro inverter.
Figure 2. SkySun Ganged heliostat prototype
PREDICTS 1: Results of module-level power electronics accelerated testing
A project team led by Sandia
National Labs, with partners at Arizona State and TUV Rheinland, has applied a suite of accelerated life tests to module-level power electronics (MLPE) devices to predict their reliability and durability in the field. A subset of the DOE’s Physics of
Reliability: Evaluating Design Insights for Component Technologies in Solar
(PREDICTS), this project has tested 140 MLPE units from five
different manufacturers in four types of tests: damp heat, themal cycling,
static temperature (85, 100, and 125 degrees Celsius), and grid transients.
Of
10 powered devices used in damp heat testing, 2 failed at 1240-1550 hours and 2
failed at 4369-4876 hours. The remaining 6 units were still operating at full
power-handling levels even after 5380 hours of the damp heat test. A wealth of
historical usage data exists for damp heat degradation and failure rate
differences of modules; a lack of historical usage data for MLPEs has hindered data comparison, until now. Comparing data from PVs and MLPEs is useful because they have similar usage environments and design-life expectations. Based on historical testing data, most PV modules fail or degrade
significantly (>10%) before 3,000 hours of damp-heat accelerated testing (Figure 3). That MLPE
units have degraded significantly less than modules with fewer failures after
3,000+ hours of testing indicates that these devices may outlive their attached PV modules in the field under normal
usage conditions.
In thermal cycling testing, only one of the 10 powered
units failed through 836 thermal cycles. Units showed efficiency degradations
<10% after more than 800 thermal cycles (Figure 4). This result compares favorably with PV module failure and degradation during long-term thermal cycle testing, reinforcing the robustness
of MLPEs in the field.
Figure 3. Relative power at MPP of 7 different c-Si modules after Damp Heat testing (Koehl, PVSC2013) compared to full load testing of MLPE units (normalized efficiency)
Figure 4. MLPE efficiencies at 50oC UUT over time during thermal cycle testing. Units did show some efficiency degradation over time.
Sandia Labs and NREL hosted successful 2016 PV
Reliability Workshop
Sandia
National Laboratories and the National Renewable Energy Laboratory (NREL) jointly
organized the annual Photovoltaic
Module Reliability Workshop.The February
23-25, 2016, event attracted nearly 300 photovoltaics (PV) experts from
national laboratories, industry, and academia
to discuss advances in photovoltaic system reliability and lifetime. Sandia’s
Olga Lavrova gave the keynote presentation, “Overview of PV Systems
Reliability”; Geoff Klise spoke on “PV System Reliability: An Operations and
Management Perspective”; Jay Johnson presented “Arc-Fault Detection and
Mitigation”; and Jack Flicker spoke on “Ground Faults Detection and Mitigation.” Concurrent technical workgroup meetings focused on emerging topics such as
rapid shutdown for PV systems for firefighter safety. The next PV materials and
reliability workshop is planned for February 2017.
Preliminary results of bifacial project released on the PVPMC web site
Sandia Labs is leading a three-year research project to study
bifacial photovoltaic technology and performance. The project is sponsored by
SunShot, a U.S. Department of Energy program. Conventional PV cells convert
light hitting the front side of the cell to electricity. With a few extra
processing steps, bifacial PV cells can be made so that light hitting from both
sides contributes to the current produced by the cell. When cells are placed
into modules designed with transparent back sheets or glass-glass construction,
bifacial PV modules are born. This idea is not new, but this technology has,
thus far, remained on the fringe of the PV market. We believe that bifacial PV
may begin to enter the mainstream of the PV industry in the next few years,
especially if its levelized cost of energy is lower than monofacial
technologies. Field data, characterization methods and standards, and predictive
models are needed.
Fault Detection Tool Project: Automated discovery of faults using machine learning
Advanced monitoring of photovoltaic (PV) systems can ensure efficient operations, but extensive monitoring of large quantities of data can be cumbersome for the individual analyst. To address this challenge, Sandia National Laboratories' Fault Detection Tool project uses machine-learning algorithms embedded into data collection devices on-site or at a central server to detect performance issues and failures automatically. The algorithms, based on machine learning techniques such as Gaussian Process (GP), Laterally Primed Adaptive Resonance Theory (LAPART), and Support Vector Machines, can be used to detect and classify faults. In this work, a programmable data storage device, Raspberry Pi (RPI), was placed in-situ with an actual PV array (see Figure 5).
Figure 5. Raspberry Pi (RPI) device used to monitor a PV array
The data was stored in a local database. Using a GP algorithm, researchers accurately estimated performance. Based on the residuals between the computed estimate and the actual values, the fault detection tool classified the behavior as normal or in a fault condition. This machine-learning approach not only identified fault behavior, but also defined the lost energy revenue caused by the fault condition.
Sandia uses a few spectral measurements to enhance PV performance models
The power generated by a PV
cell depends on both the intensity and spectrum of the light incident on the
cell. Silicon solar cells, for example, respond to both visible and
near-infrared light, but differently: red light has more impact on PV power
output than blue light. Accurately predicting power output requires accounting
for the effect of spectrum on the PV cell’s power. However, measuring the
entire incident solar spectrum is an expensive and data intensive task.
We explored whether PV
performance modeling can be improved by using spectral measurements at only a
few wavelengths rather than measuring the full spectrum. The analysis used data
collected at Los Alamos and Albuquerque, New Mexico. When irradiance measurements at two wavelengths
were included in the performance models, errors were reduced by up to a factor
of two compared to modeling that used air mass as a proxy for the effects of
spectrum, as is current practice in the PV modeling community. The important
wavelengths were relatively consistent across different cell technologies
suggesting that measurements at a few specific spectral wavelengths may improve
the accuracy of performance modeling for all types of PV cells.
Fig. 6. Plot showing curves of spectral intensity of incident sunlight at each wavelength. Strong vertical color gradients at a specific wavelength indicate that wavelength is important for PV performance modeling.
The plot in Figure 6 shows curves of spectral
intensity of incident sunlight at each wavelength, with colors indicating the
short circuit current (Isc, an input to PV power models) at the instant each spectral
intensity curve was captured. Strong vertical color gradients (dark to light,
or light to dark) at a specific wavelength (e.g., 600nm) indicate that
wavelength is important for PV performance modeling. Measurements of spectral
intensity at only a few of these important wavelengths can help reduce PV
performance modeling errors. This analysis will be presented at the
Photovoltaic Specialists Conference (PVSC) in June 2016.
J. Neely, J. Johnson, J.
Delhotal, S. Gonzalez, M. Lave, Evaluation of PV Frequency-Watt Function for
Fast Frequency Reserves, IEEE Applied Power Electronics Conference (APEC), Long
Beach, CA, March 20-24, 2016.
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