Stereo high-speed video of photovoltaic modules undergoing laboratory hail tests was processed using digital image correlation to determine module surface deformation during and immediately following impact. The purpose of this work was to demonstrate a methodology for characterizing module impact response differences as a function of construction and incident hail parameters. Video capture and digital image analysis were able to capture out-of-plane module deformation to a resolution of ±0.1 mm at 11 kHz on an in-plane grid of 10 × 10 mm over the area of a 1 × 2 m commercial photovoltaic module. With lighting and optical adjustments, the technique was adaptable to arbitrary module designs, including size, backsheet color, and cell interconnection. Impacts were observed to produce an initially localized dimple in the glass surface, with peak deflection proportional to the square root of incident energy. Subsequent deformation propagation and dissipation were also captured, along with behavior for instances when the module glass fractured. Natural frequencies of the module were identifiable by analyzing module oscillations postimpact. Limitations of the measurement technique were that the impacting ice ball obscured the data field immediately surrounding the point of contact, and both ice and glass fracture events occurred within 100 μs, which was not resolvable at the chosen frame rate. Increasing the frame rate and visualizing the back surface of the impact could be applied to avoid these issues. Applications for these data include validating computational models for hail impacts, identifying the natural frequencies of a module, and identifying damage initiation mechanisms.
Hail poses a significant threat to photovoltaic (PV) systems due to the potential for both cell and glass cracking. This work experimentally investigates hail-related failures in Glass/Backsheet and Glass/Glass PV modules with varying ice ball diameters and velocities. Post-impact Electroluminescence (EL) imaging revealed the damage extent and location, while high-speed Digital Image Correlation (DIC) measured the out-of-plane module displacements. The findings indicate that impacts of 20 J or less result in negligible damage to the modules tested. The thinner glass in Glass/Glass modules cracked at lower impact energies (-25 J) than Glass/Backsheet modules (-40 J). Furthermore, both module types showed cell and glass cracking at lower energies when impacted at the module's edges compared to central impacts. At the time of presentation, we will use DIC to determine if out-of-plane displacements are responsible for the impact location discrepancy and provide more insights into the mechanical response of hail impacted modules. This study provides essential insights into the correlation between impact energy, impact location, displacements, and resulting damage. The findings may inform critical decisions regarding module type, site selection, and module design to contribute to more reliable PV systems.
Photovoltaic modules undergoing laboratory hail tests were observed using high speed video to analyze the key characteristics of impact-induced glass fracture, including crack onset time, initiation location relative to the impact site, and propagation trends. Fifteen commercially representative glass-glass thin-film modules were recorded at 300,000 frames per second during hail impacts which happened to cause glass fracture. Images were processed to identify the time between impact and first plausible glass crack appearance (average 126 μs, standard deviation 59 μs) along with the time to a confirmed crack (average 158 μs, standard deviation 77μs), during the ice ball impacts which had a median kinetic energy of 47 J delivered by 55 mm diameter balls. Limiting factors for identifying glass crack timings were ice ball fragmentation obscuring the impact site and indistinct initial crack appearance, which were inherent to the images and not improved with processing. Computational simulations corresponding to each impact event showed that glass stresses were still localized to the impact site during times with definitively identifiable fracture, and even impacts which did not induce failure created local stress magnitudes exceeding stress levels associated with static glass fracture. These observations confirm that impact-induced glass failure is a time-and rate-dependent phenomena. Results from this study provide baseline metrics for developing a glass fracture criterion to predict module damage during hail impact events, which in turn allows for analysis of design features that may affect damage susceptibility.
Concentrating solar power (CSP) plants with integrated thermal energy storage (TES) have successfully been coupled with photovoltaics (PV) + chemical battery energy storage (BES) in recent commercial-scale projects to balance system cost and diurnal power availability. Sandia National Laboratories has been tasked with designing an advanced solar energy system to power Kirtland Air Force Base (KAFB) where Sandia is co-located in Albuquerque, NM, USA. This design process requires optimization of individual components and capacities of the hybrid system. Preliminary modeling efforts have shown that a hybrid CSP+TES/PV+BES in Albuquerque, NM is sufficient for net-zero power generation for Sandia/KAFB for the next decade. However, the ability to meet the load in real-time (and minimize energy export) requires balance of generation and storage assets. Our results also show that excess PV used to charge TES improves resilience and overall renewables-to-load for the system. Here we will present the results of a parametric study varying the land use proportions of CSP and PV, and TES and BES capacities. We evaluate the effects of these variables on energy generation, real-time load satisfaction, site resilience to grid outages, and LCOE, to determine viable hybrid solar energy designs and their cost implications.
In this work, we introduce and compare the results of several methods for determining the horizon profile at a PV site, and compare their use cases and limitations. The methods in this paper include horizon detection from time-series irradiance or performance data, modeling from GIS topology data, manual theodolite measurements, and camera-based horizon detection. We compare various combinations of these methods using data from 4 Regional Test Center sites in the US, and 3 World Bank sites in Nepal. The results show many differences between these methods, and we recommend the most practical solutions for various use-cases.
Snow is a significant challenge for PV plants at northern latitudes, and snow-related power losses can exceed 30 % of annual production. Accurate loss estimates are needed for resource planning and to validate mitigation strategies, but this requires accurate snow detection at the inverter level. In this study, we propose and validate a framework for detecting snow in time-series inverter data. We identify four distinct snow-related power loss modes based on the inverter's operating points and electrical properties of the inverter and PV arrays. We validate these modes and identify their associated physical snow conditions using site images. Finally we examine relative frequencies of the snow power loss modes and their contributions to total power loss.
We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.
We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.
This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180^{\circ } arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N-dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.