Publications

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The Sandia Array Performance Model (SAPM), a semi-empirical model for predicting PV system power, has been in use for more than a decade. While several studies have presented laboratory intercomparisons of measurements and analysis, detailed procedures for determining model coefficients have never been published. Independent test laboratories must develop in-house procedures to determine SAPM coefficients, which contributes to uncertainty in the resulting models. In response to requests from commercial laboratories and module manufacturers, Sandia has formally documented the measurement and analysis methods as a supplement to the original model description. In this paper we present a description of the measurement procedures and an example analysis for calibrating the SAPM.

The Sandia Array Performance Model (SAPM), a semi-empirical model for predicting PV system power, has been in use for more than a decade. While several studies have presented laboratory intercomparisons of measurements and analysis, detailed procedures for determining model coefficients have never been published. Independent test laboratories must develop in-house procedures to determine SAPM coefficients, which contributes to uncertainty in the resulting models. In response to requests from commercial laboratories and module manufacturers, Sandia has formally documented the measurement and analysis methods as a supplement to the original model description. In this paper we present a description of the measurement procedures and an example analysis for calibrating the SAPM.

Determination of module temperature coefficients for voltage, current and power requires measuring the average of cell temperatures. Conventional practice is to place thermocouples or resistive temperature devices (RTDs) at a few locations on a module's back surface and to average the readings, which may not accurately represent the average temperature over all cells. We investigate the suitability of averaging RTDs, which measure average temperature along a 1m length, to accurately measure the average cell temperature when determining temperature coefficients outdoors.

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.

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Commonly used performance models, such as PVsyst, Sandia Array Performance Model (SAPM), and PV LIB, treat the PV array as being constructed of identical modules. Each of the models attempts to account for mismatch losses by applying a simple percent reduction factor to the overall estimated power. The present work attempted to reduce uncertainty of mismatch losses by determining a representative set of performance coefficients for the SAPM that were developed from a characterization of a sample of modules. This approach was compared with current practice, where only a single module’s thermal and electrical properties are testing. However, the results indicate that minimal to no improvements in model predictions were achieved.

The texture or patterning of soil on PV surfaces may influence light capture at various angles of incidence (AOI). Accumulated soil can be considered a microshading element, which changes with respect to AOI. Laboratory deposition of simulated soil was used to prepare test coupons for simultaneous AOI and soiling loss experiments. A mixed solvent deposition technique was used to consistently deposit patterned test soils onto glass slides. Transmission decreased as soil loading and AOI increased. Dense aggregates significantly decreased transmission. However, highly dispersed particles are less prone to secondary scattering, improving overall light collection. In order to test AOI losses on relevant systems, uniform simulated soil coatings were applied to split reference cells to further examine this effect. The measured optical transmission and area coverage correlated closely to the observed ISC. Angular losses were significant at angles as low as 25°.

The Sandia Array Performance Model (SAPM), a semi-empirical model for predicting PV system power, has been in use for more than a decade. While several studies have presented comparisons of measurements and analysis results among laboratories, detailed procedures for determining model coefficients have not yet been published. Independent test laboratories must develop in-house procedures to determine SAPM coefficients, which contributes to uncertainty in the resulting models. Here we present a standard procedure for calibrating the SAPM using outdoor electrical and meteorological measurements. Analysis procedures are illustrated with data measured outdoors for a 36-cell silicon photovoltaic module.

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Reflection losses from a PV module become increasingly pronounced at solar incident angles >60°. However, accurate measurement in this region can be problematic due to tracker articulation limits and irradiance reference device calibration. We present the results of a measurement method enabling modules to be tested over the full range of 0-90° by articulating the tracker in elevation only. This facilitates the use of a shaded pyranometer to make a direct measurement of the diffuse component, reducing measurement uncertainty. We further present the results of a real-time intercomparison performed by two independent test facilities ∼10 km apart.

The texture or patterning of soil on PV surfaces may influence light capture at various angles of incidence. Accumulated soil can be considered a micro-shading element, which changes with respect to AOI. While scattering losses at this scale would be significant only to the most sensitive devices, micro-shading could lead to hot spot formation and other reliability issues. Indoor soil deposition was used to prepare test coupons for simultaneous AOI and soiling loss experiments. A mixed solvent deposition technique was used to consistently deposit patterned test soils onto glass slides. Transmission decreased as soil loading and AOI increased. Highly dispersed particles are less prone to secondary scattering, improving overall light collection.

PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.

The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

The Advanced Measurement and Analysis of PV Derate Factors project focuses on improving the accuracy and reducing the uncertainty of PV performance model predictions by addressing a common element of all PV performance models referred to as “derates”. Widespread use of “rules of thumb”, combined with significant uncertainty regarding appropriate values for these factors contribute to uncertainty in projected energy production.

The Characterizing Emerging Technologies project focuses on developing, improving and validating characterization methods for PV modules, inverters and embedded power electronics. Characterization methods and associated analysis techniques are at the heart of technology assessments and accurate component and system modeling. Outputs of the project include measurement and analysis procedures that industry can use to accurately model performance of PV system components, in order to better distinguish and understand the performance differences between competing products (module and inverters) and new component designs and technologies (e.g., new PV cell designs, inverter topologies, etc.).

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Soil accumulation on photovoltaic (PV) modules presents a challenge to long-term performance prediction and lifetime estimates due to the inherent difficulty in quantifying small changes over an extended period. Low mass loadings of soil are a common occurrence but remain difficult to quantify. In order to more accurately describe the specific effects of sparse soil films on PV systems, we have expanded upon an earlier technique to measure the optical losses due to an artificially applied obscurant film. A synthetic soil analog was sprayed onto glass coupons at very brief intervals with a high-volume, low-pressure pneumatic sprayer. Light transmission through the grime film was evaluated using a quantum efficiency test stand and UV/vis spectroscopy. A 0.1-g/m 2 grime loading was determined to be the limit of mass measurement sensitivity, which is similar to some reports of daily soil accumulation. Predictable, linear decreases in transmission were observed for samples with a mass loading between 0.1 and 0.5 g/m2. A similar change was observed for soiled coupons from an outdoor monitoring station. Collected soil from the field coupons was analyzed to develop a compositional analog for indoor studies. Natural and synthetic soils produced similar decreases in transmission.

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The soiling losses on high concentrating photovoltaic (HCPV) systems may be influenced by the spectral properties of accumulated soil. We 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. Moreover, 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.

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IEEE Standard 1547-2003 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.

Soil accumulation on photovoltaic (PV) modules presents a challenge to long-term performance prediction and lifetime estimates due to the inherent difficulty in quantifying small changes over an extended period. Low mass loadings of soil are a common occurrence, but remain difficult to quantify. In order to more accurately describe the specific effects of sparse soil films on PV systems, we have expanded upon an earlier technique to measure the optical losses due to an artificially applied obscurant film. A synthetic soil analogue consisting of AZ road dust and soot in acetonitrile carrier solvent was sprayed onto glass coupons at very brief intervals with a high volume, low pressure pneumatic sprayer. Light transmission through the grime film was evaluated using a QE test stand and UV/vis spectroscopy. A 0.1 g/m2 grime loading was determined to be the limit of mass measurement sensitivity, which is similar to some reports of daily soil accumulation. Predictable, linear decreases in transmission were observed for samples with a mass loading between 0.1 and 0.5 g/m2. Reflectance measurements provided the best means of easily distinguishing this sample from a reference.

This manuscript is intended to serve as a practical guide to conducting repeatable indoor soiling experiments for PV applications. An outline of techniques, materials and equipment used in prior studies [1-3] is presented. Additional recommendations and practical guidance has been presented. Major sections include techniques to formulate soil simulants, ('standard grime') and feedstocks from traceable components, spray application, and quantitative measurement methodologies at heavy and minimal soil loadings.

The accumulation of soil on photovoltaic (PV) modules may introduce a spectral loss due to the color profile of the accumulated material. In order to compare the spectral and total losses experienced by a cell, soil analogs were formulated to contain common mineral pigments (Fe-2O 3 and göthite) with previously developed 'standard grime' mixtures. These mixtures simulated a wide range of desert soil colors and were applied to glass test coupons. The light transmission through the deposited film was evaluated by UV/vis/NIR spectroscopy and by placing the coupon over a test cell in a 1-sun simulator and quantum efficiency test stand. Distinct peaks in the 300-600-nm range were observed by UV/vis/NIR spectroscopy corresponding to the Fe 2 O3 and göthite. Approximately analogous features were noted in the QE measurement. Overall comparisons were made by integrating the response of a soiled coupon relative to a clean reference. Soils rich in red pigments (Fe2 O3) caused a greater integrated response than soils rich in yellow pigment (göthite). The yellow soils caused a greater attenuation in a specific region of the spectrum (300-450 nm), which may have significant implications to specific devices, such as multijunction and CdTe technologies. © 2011-2012 IEEE.

The solar spectrum varies with atmospheric conditions and composition, and can have significant impacts on the output power performance of each junction in a concentrating solar photovoltaic (CPV) system, with direct implications on the junction that is current-limiting. The effect of changing solar spectrum on CPV module power production has previously been characterized by various spectral performance parameters such as air mass (AM) for both single and multi-junction module technologies. However, examinations of outdoor test results have shown substantial uncertainty contributions by many of these parameters, including air mass, for the determination of projected power and energy production. Using spectral data obtained from outdoor spectrometers, with a spectral range of 336nm-1715nm, this investigation examines precipitable water (PW), aerosol and dust variability effects on incident spectral irradiance. This work then assesses air mass and other spectral performance parameters, including a new atmospheric component spectral factor (ACSF), to investigate iso-cell, stacked multijunction and single-junction c-Si module performance data directly with measured spectrum. This will then be used with MODTRAN5® to determine if spectral composition can account for daily and seasonal variability of the short-circuit current density Jsc and the maximum output power Pmp values. For precipitable water, current results show good correspondence between the modeled atmospheric component spectral factor and measured data with an average rms error of 0.013, for all three iso-cells tested during clear days over a one week time period. Results also suggest average variations in ACSF factors with respect to increasing precipitable water of 8.2%/cmH2O, 1.3%/cmH2O, 0.2%/cmH2O and 1.8%/cmH2O for GaInP, GaAs, Ge and c-Si cells, respectively at solar noon and an AM value of 1.0. For ozone, the GaInP cell had the greatest sensitivity to increasing ozone levels with an ACSF variation of 0.07%/cmO3. For the desert dust wind study, consistent ACSF behavior between all iso-cells and c-Si was found, with only significant reductions beyond 40mph.

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