Predictive Reliability for AC Photovoltaic Modules Based on Electro-Thermal Phenomena
Abstract not provided.
Publications
Performance Model for Characterizing AC Modules and Predicting Their Power
Abstract not provided.
Recent Advances in Outdoor Measurement Techniques for Angle of Incidence Effects
Abstract not provided.
Mapping HCPV Module or System Response to Solar Incident Angle
Abstract not provided.
Predicting the spectral effects of soils on high concentrating photovoltaic systems
Solar Energy
Soiling losses on high concentrating photovoltaic (HCPV) systems may be influenced by the spectral properties of accumulated soil. We have predicted the response of an isotype cell to changes in spectral content and reduction in transmission due to soiling using measured UV/vis transmittance through soil films. Artificial soil test blends deposited on glass coupons were used to supply the transmission data, which was then used to calculate the effect on model spectra. The wavelength transparency of the test soil was varied by incorporating red and yellow mineral pigments into graded sand. The more spectrally responsive (yellow) soils were predicted to alter the current balance between the top and middle subcells throughout a range of air masses corresponding to daily and seasonal variation.
Predictive Reliability for AC Photovoltaic Modules Based on Electro-Thermal Phenomena
Abstract not provided.
Sun-relative pointing for dual-axis solar trackers employing azimuth and elevation rotations
Journal of Solar Energy Engineering, Transactions of the ASME
We present an algorithm to calculate the azimuth and elevation angles for a dual axis tracker to be pointed away from the sun with a desired orientation between the sun and the tracker face. Desired tracker positions are specified in terms of angle of incidence (AOI) and AOI direction, i.e., the direction of the projection of the sun beam onto the plane of the tracker face. This algorithm was developed to enable characterization of the electro-optical response of photovoltaic (PV) and concentrating PV (CPV) modules with anisotropic response to AOI.
Photovoltaic Microinverter Testbed for Multiple Device Interoperability
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.
Sampling and Filtering in Photovoltaic System Performance Monitoring
The performance of photovoltaic systems must be monitored accurately to ensure profitable long-term operation. The most important signals to be measured—irradiance and temperature, as well as power, current and voltage on both DC and AC sides of the system—contain rapid fluctuations that are not observable by typical monitoring systems. Nevertheless these fluctuations can affect the accuracy of the data that are stored. This report closely examines the main signals in one operating PV system, which were recorded at 2000 samples per second. It analyzes the characteristics and causes of the rapid fluctuations that are found, such as line-frequency harmonics, perturbations from anti-islanding detection, MPPT searching action and others. The operation of PV monitoring systems is then simulated using a wide range of sampling intervals, archive intervals and filtering options to assess how these factors influence data accuracy. Finally several potential sources of error are discussed with real-world examples.
Introduction to the Open Source PV LIB for Python Photovoltaic System Modeling Package
Abstract not provided.
2014 IEEE PVSC Tutorial on PV System Performance Modeling
Abstract not provided.
Determining the Effect of Temperature on Microinverter Inversion Efficiency
Abstract not provided.
HCPV characterization: Analysis of fielded system data
AIP Conference Proceedings
Sandia and Semprius have partnered to evaluate the operational performance of a 3.5 kW (nominal) R&D system using 40 Semprius modules. Eight months of operational data has been collected and evaluated. Analysis includes determination of Pmp, Imp and Vmp at CSTC conditions, Pmp as a function of DNI, effect of wind speed on module temperature and seasonal variations in performance. As expected, on-sun Pmp and Imp of the installed system were found to be ~10% lower than the values determined from flash testing at CSTC, while Vmp was found to be nearly identical to the results of flash testing. The differences in the flash test and outdoor data are attributed to string mismatch, soiling, seasonal variation in solar spectrum, discrepancy in the cell temperature model, and uncertainty in the power and current reported by the inverter. An apparent limitation to the degree of module cooling that can be expected from wind speed was observed. The system was observed to display seasonal variation in performance, likely due to seasonal variation in spectrum.
Predicting the Spectral Effects of Soils on Multijunction Photovoltaic Systems
Abstract not provided.
Performance model for Semprius module RDD-MOD-296 PSEL 2941
Abstract not provided.
Flat plate concentrators with large acceptance angle enabled by micro cells and mini lenses: performance evaluation
Abstract not provided.
Testing and Characterization of PV Modules with Integrated Microinverters
Abstract not provided.
Determining Coefficients for the Sandia Array Performance Model
Abstract not provided.
Photovoltaic Prognostics and Health Management using Learning Algorithms
Abstract not provided.
Calibration of the Sandia Array Performance Model Using Indoor Measurements
Abstract not provided.
Design for Reliability: A Low Concentration PV Case Study
Abstract not provided.
Design for Reliability: A Low Concentration PV Case Study
Abstract not provided.
Comparison of a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model
Abstract not provided.
Performance model assessment for multi-junction concentrating photovoltaic systems
Four approaches to modeling multi-junction concentrating photovoltaic system performance are assessed by comparing modeled performance to measured performance. Measured weather, irradiance, and system performance data were collected on two systems over a one month period. Residual analysis is used to assess the models and to identify opportunities for model improvement. Large photovoltaic systems are typically developed as projects which supply electricity to a utility and are owned by independent power producers. Obtaining financing at favorable rates and attracting investors requires confidence in the projected energy yield from the plant. In this paper, various performance models for projecting annual energy yield from Concentrating Photovoltaic (CPV) systems are assessed by comparing measured system output to model predictions based on measured weather and irradiance data. The results are statistically analyzed to identify systematic error sources.
Performance model assessment for multi-junction concentrating photovoltaic systems
Four approaches to modeling multi-junction concentrating photovoltaic system performance are assessed by comparing modeled performance to measured performance. Measured weather, irradiance, and system performance data were collected on two systems over a one month period. Residual analysis is used to assess the models and to identify opportunities for model improvement.
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