Engineering topics
Braid, Jennifer L.
Publications and source records attributed to Braid, Jennifer L..
Photovoltaic inverter-based quantification of snow conditions and power loss
Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.
Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries
We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate ( PLR ) of −0.27 ± 0.12% per annum (%/ a ) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.
Overall Performance Losses and Activated Mechanisms in Double Glass and Glass-backsheet Photovoltaic Modules with Monofacial and Bifacial PERC Cells, under Accelerated Exposures
Commercial PV modules have various packaging choices nowadays, which influence their long-term reliability. This study compared the degradation behaviors of sixteen module variants from two brands with varying encapsulant materials (EVA or POE), encapsulant types, module architectures (GB or DG), and cell types (monofacial or bifacial) using null hypothesis testing to determine statistical significant findings. The modules were exposed for 2,520 hours under two accelerated exposures: modified damp heat (mDH) and modified damp heat with full-spectrum light (mDH+FSL). For both brands, two DG module variants with UV-Cutoff rear encapsulant are found to have significantly lower average power loss than the module variants of EVA+GB with opaque rear encapsulant after each accelerated exposure. Metallization interconnect corrosion is identified as the primary degradation mechanism. Furthermore, unsupervised hierarchical clustering finds that the degradation behaviors of modules from one brand with a more strict manufacturing quality control depends on module architectures only.
Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling
As the PV industry is rapidly expanding, it is important to thoroughly investigate the long-term impact of packaging strategies on the performance of PV modules. In this study, the variants in sets differ on the basis of manufacturer (A/B), encapsulant (EVA/POE), rear encapsulant (UV-cutoff/opaque/transparent), module architecture (GB/DG) and cell type (monofacial/bifacial). The minimodules were exposed for 2520 hours in modified damp heat, with or without full spectrum light. Every 504 hours, stepwise electrical characterization techniques were employed to track changes in minimodules. Degradation pathway modeling using network structural equation modeling was employed to study pairwise relationships between variables and service lifetime prediction in minimodules. Through this study, differences in quality control are identified in minimodules made by different manufacturers. Minimodules with UV-cutoff rear encapsulant show relatively better stability, whereas the ones with opaque rear encapsulant show greater power loss. In addition, GB having UV-cutoff rear encapsulation and GB with POE having opaque rear encapsulation were identified to be stable as they lack a best model fit. Here, the primary power loss mechanism in degrading variants is interconnect corrosion.
Facilitating Large‐Scale Snow Shedding from In‐Field Solar Arrays using Icephobic Surfaces with Low‐Interfacial Toughness
Abstract Large‐scale accrual of snow and ice on solar arrays in northern latitudes can cause significant power generation losses during winter. Depending on environmental conditions, snow can encompass a wide range in physical characteristics from dry snow (modulus ≈100 kPa and density ≈0.1 g cm −3 ) to bulk ice (modulus ≈8 GPa and density ≈0.9 g cm −3 ). This variation in snow morphology has made the development of a passive, broad‐spectrum, snow and ice‐shedding surface challenging. Here, the authors develop one of the first surfaces that simultaneously possesses both low‐interfacial strength ( τ˄ ice < 50 kPa) and toughness (Γ ice < 0.5 J m −2 ) with ice. These surfaces, fabricated via the addition of mobile polymer chains/oils to a thin polymeric coating, require extremely low detachment forces for ice, enabling its passive shedding at virtually any accretion length scale. Preliminary evidence that the new surfaces can shed different forms of snow and ice from field‐deployed solar arrays, over a range of subzero temperatures for several weeks, leading to significant increases in power generation is provided. The optically transparent surfaces are easily scalable and can be widely deployed by the solar industry in areas that see persistent snow. Other applications include automotive windshields, LIDAR covers for autonomous vehicles, and cold climate optical sensors.
Solar Transposition Modeling via Deep Neural Networks With Sky Images
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° 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. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.
Degradation mechanisms and partial shading of glass-backsheet and double-glass photovoltaic modules in three climate zones determined by remote monitoring of time-series current–voltage and power datastreams
Degradation and partial shading impact the long-term reliability and power production of photovoltaic (PV) modules and power plants. Time-series power (P mp ) and current–voltage (I-V) curve datastreams from PV modules enable a remote diagnostic approach to quantify active degradation mechanisms and identify partial shading. We study three to nine years of these datastreams, including 3.6 million P mp I-V curves and 36 million values, from eight PV modules, four each of double-glass and glass-backsheet module architectures, located in three distinctly different Köppen-Geiger climate zones, to determine the module’s performance loss rates (PLR), identify active degradation mechanisms and power loss modes, along with partial shading by local objects. Considering both module architectures, PLR results indicate that the BSh climate zone is the most aggressive for module degradation, while the Alpine ET zone is the mildest climate. PLR of double-glass modules located in BWh and BSh climate zones are different due to the significantly greater uniform current loss (ΔP Isc ) for double-glass modules in BSh, at a 5% significance level. Power loss for four out of five modules located in the BWh and BSh climates are dominated by uniform current degradation. Statistical analysis of multistep I-V curves detects partial shading experienced by three studied modules with details of the shading profile, the shading Poynting vector diagram for the obstacle’s relative position, shading scenarios, and duration. Lastly, this work demonstrates how remote monitoring and diagnosis of P mp & I-V time-series of modules can provide quantitative operations and maintenance insights into system performance, degradation mechanisms, and shading.
Reliability and Power Degradation Rates of PERC Modules Using Differentiated Packaging Strategies and Characterization Tools
The reliability, durability and lifetime performance of passivated emitter, rear cell (PERC) modules used in real-world PV power plants is a critical challenge underlying the rapid adoption and bankability of these PERC cells, whose high efficiency help reduce the levelized cost of electricity (LCOE). We propose a degradation-science study of PERC module degradation pathways, benchmarking them relative to known degradation mechanisms and pathways of the incumbent aluminum back surface field (Al-BSF) modules exposed to real-world and accelerated exposure conditions.
Mechanistic Insights to Degradation of PERC Minimodules with Differentiated Packaging Materials & Module Architectures
In this paper, we study the degradation behavior of glass-backsheet (GB) and double glass (DG) multicrystalline sil- icon monofacial PERC minimodules, fabricated using polyolefin elastomer (POE) and ethylene vinyl acetate (EVA) encapsulants, undergoing exposures of modified damp-heat (80 °C / 85% RH), with and without full spectrum light. The completed exposure time, at this time, is 1512 hours with each step of 504 hours. Step- wise measurements are conducted throughout exposures including current-voltage (I-V ) curves, Suns-V oc , electroluminescence (EL) images, and four-point proof loading with EL for tracking changes in mechanical properties in encapsulants by cell fracture probability. The results show that GB minimodules of both encapsulants, compared to DG minimodules, experience more changes in features of I-V and Suns-V oc measurements. There is no development of PV cell cracking during proof loading as seen from EL images and the load versus load line displacement plots currently.
Spatially resolved characterization of optical and recombination losses for different industrial silicon solar cell architectures
In this work, spatially resolved characterization methods are used to identify loss mechanisms for common p -type silicon solar cell architectures, including multicrystalline aluminum back surface field (Al-BSF), monocrystalline Al-BSF, monocrystalline passivated emitter and rear cells (PERC), and bifacial monocrystalline PERC. The characterization methods used in this work include suns-V OC , photoluminescence imaging, and spatially resolved external quantum efficiency and reflectance measurements. The optical and recombination losses are driven by the material properties, cell processing conditions, and device architecture. These losses are quantified and categorized in terms of underlying mechanisms (e.g., front reflectance, escape reflectance, front recombination, and parasitic optical absorption and recombination in the bulk and rear). The ability to create images of these loss parameters can be used to gain more insight into the materials and manufacturing processes used to produce solar cells, and examples are given in this work to illustrate how these images can help reveal the origin of defects.
Direct nanoscale mapping of open circuit voltages at local back surface fields for PERC solar cells
The open circuit voltage (V OC) is a critical and common indicator of solar cell performance as well as degradation, for panel down to lab-scale photovoltaics. Detecting V OC at the nanoscale is much more challenging, however, due to experimental limitations on spatial resolution, voltage resolution, and/or measurement times. Accordingly, in this work an approach based on Conductive Atomic Force Microscopy is implemented to directly detect the local V OC , notably for monocrystalline Passivated Emitter Rear Contact (PERC) cells which are the most common industrial-scale solar panel technology in production worldwide. This is demonstrated with cross-sectioned monocrystalline PERC cells around the entire circumference of a poly-Aluminum-Silicide via through the rear emitter. The V OC maps reveal a local Back Surface Field extending ~2 μm into the underlying p-type Si absorber due to Al in-diffusion as designed. Such high spatial resolution methods for photovoltaic performance mapping are especially promising for directly visualizing the effects of processing parameters, as well as identifying signatures of degradation for silicon and other solar cell technologies.
Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images
Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.