Snow Mechanics

This article studies the anti-ice performance of several micro/nano-rough hydrophobic coatings with different surface chemistry and topography. The coatings were prepared by spin-coating or dip coating and used organosilane, fluoropolymer or silicone rubber as a top layer. Artificially created glaze ice, similar to the naturally accreted one, was deposited on the nanostructured surfaces by spraying supercooled water microdroplets (average size ∼80 m) in a wind tunnel at subzero temperature (−10 • C). The ice adhesion strength was evaluated by spinning the samples in a centrifuge at constantly increasing speed until ice delamination occurred. The results show that the anti-icing properties of the tested materials deteriorate, as their surface asperities seem to be gradually broken during icing/de-icing cycles. Therefore, the durability of anti-icing properties appears to be an important point for further research. It is also shown that the anti-icing efficiency of the tested superhydrophobic surfaces is significantly lower in a humid atmosphere, as water condensation both on top and between surface asperities takes place, leading to high values of ice adhesion strength. This implies that superhydrophobic surfaces may not always be ice-phobic in the presence of humidity, which can limit their wide use as anti-icing materials.

Overhead power line conductors and ground wires are affected by ice and snow accretion which can easily adhere to their surface, causing the breakage of cables and the collapse of pylons due to excessive weight. In Italy, the main concern is about wet snow: this phenomenon occurs close to zero degrees Celsius with snow density reaching up to 350 Kg/m3. Anti-icing and anti-snow coatings represent a possible strategy to mitigate ice accretion on overhead power line structures. Many works are oriented to achieve anti-icing properties, starting from superhydrophobic coatings or slippery coatings; however, there is a lack of knowledge about the anti-snow behaviour of these surfaces. In this work, aluminium alloy conductor and ground-wire samples were prepared with different coatings, which include hydrophobic, superhydrophobic and slippery surfaces prepared in the laboratory. Characterisations of sample wettability at room and low temperatures and ice adhesion strength were carried out i...

Solar photovoltaic (PV) systems are frequently installed in climates with significant snowfall. To better understand the effects of snowfall on the performance of PV systems, a multi-angle, multi-technology PV system was commissioned and monitored over two winters. A novel methodology was introduced and validated with this system, which allows for the determination of snowfall losses from time-series performance data with correlated meteorological observations down to a 5-minute resolution. In addition, a new method for determining the probability distribution of snow deposition on a module from image data was developed. It was found that the losses due to snowfall are dependent on the angle and technology being considered and the effects of increased albedo in the surroundings of a PV system can increase expected yields, particularly in the case of high tilt angle systems. Existing methods for predicting losses due to snowfall were investigated, and were found to provide overly conservative estimates of snow losses. Overall the results show that the proper assessment of snow related losses can help improve system performance and maintenance. It is concluded that proper characterization of the snowfall effect on PV system performance can influence better systems optimization for climates experiencing snowfall.

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems.

Blowing snow accounts for a large part of Illinois Department of Transportation’s total winter maintenance expenditures. This project aims to develop recommendations on the design and placement of living snow fences (LSFs) to minimize snowdrift on Illinois highways. The research team examined historical IDOT data for resource expenditures, conducted a literature review and survey of northern agencies, developed and validated a numerical model, field tested selected LSFs, and used a model to assist LSF design. Field testing revealed that the proper snow fence setback distance should consider the local prevailing winter weather conditions, and snow fences within the right-of-way could still be beneficial to agencies. A series of numerical simulations of flow around porous fences were performed using Flow-3D, a computational fluid dynamics software. The results of the simulations of the validated model were employed to develop design guidelines for siting LSFs on flat terrain and for t...

Today energy-efficient and energy-harvesting buildings experience an ever-increasing interest and demand. Building integrated photovoltaics (BIPV) may in this respect represent a powerful and versatile tool for reaching the goal of zero energy and zero emission buildings. The BIPV systems replace the outer building envelope skin, thus serving simultanously as both a climate screen and a power source generating electricity. However, snow and ice formation on the exterior solar cell surfaces reduce their performance and may also lead to faster deterioration. Hence, if one could find a way to develop solar cells which were able to avoid snow and ice formation on their surfaces, one would have moved a large step ahead. This work presents a review exploring miscellaneous pathways for avoiding snow and ice formation on solar cell surfaces including superhydrophobic and icephobic surfaces.

Atmospheric icing occurs when surface of exposed structures come into contact with supercooled water droplets and/or snow particles. Ice and wet-snow adhesion and excessive accumulation on exposed structures and equipment is well known as a source of numerous types of failures and malfunctions in power transmission lines, aircrafts, boats etc. in many cold-climate regions. Hydro-/superhydrophobic coatings have been developed over the past few decades as a passive technique to reduce or prevent ice accumulation on outdoor structures. In this study, superhydrophobic Al alloy (AA2024) substrates with static water contact angle values of (CA)>163 º and water contact angle hysteresis values of (CAH)≤5 º were prepared by etching aluminium alloy substrate in hot water followed by further surface hydrophobization with 1H,1H,2H,2H-perfluorodecyltriethoxysilane (FAS-17) using wet-chemistry technique. The prepared coated Al alloy samples showed good superhydrophobic and self-cleaning properties. Systematic study of both water-and ice-repellent behaviour of such organic coatings terminated by alkyl groups and expected to reduce ice adhesion, were then conducted. The stability tests of the coated surfaces with CA>150 º in water resulted in a gradual loss of their superhydrophobic properties after ~1280 h of immersion. Artificially created glaze ice, similar to the naturally accreted one, was deposited on the nanostructured surfaces by spraying supercooled water micro-droplets with the average size of ~80 µm in a wind tunnel at subzero temperature (−10 o C) and a wind speed of 11 m/s. The ice adhesion strength was evaluated by spinning the samples in a centrifuge at constantly increasing speed until ice delaminating occurred. While the uncoated as-received Al alloy samples were found to have an average ice detachment shear stress of ~445±20 kPa, their counterparts coated with a thin layer of FAS-17 showed a lower value of ~65 kPa. This reduction is attributed to the presence of engineered micro-/nano-hierarchical surface asperities and to applying low surface energy layers on the sample surfaces. However, the results show that the anti-icing properties of the tested samples deteriorate over time.

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems.

This study aims at analyzing the hydrophobicity of a wide range of fluorine-carbon coatings, from carbon-rich to fluorine-rich materials, carried out within the framework of a search for snow-and ice-repellent materials. A simple model accounting for both surface chemical composition and roughness was applied to evaluate the wettability of various fluorocarbon coatings. Apparent contact angles of water on both smooth and rough surfaces are modeled as functions of the fluorination degree and area fraction of air entrapped into the water-solid interface. It is shown that low-fluorinated C-F coatings are hardly hydrophobic and likely will not provide snow or ice-repellent properties. At the same time, coatings with higher F concentration demonstrate good hydrophobicity, which can be further enhanced by air entrapment into rough surface structures. Thus, as coatings with good mechanical properties, low surface energy and surface roughness governed by deposition parameters, such C-F materials may be of potential use for snow-and ice-repellent purposes.

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems.

This research aims to experimentally improve the overall efficiency of solar photovoltaic (PV) panels by coating them with hydrophobic SiO 2 nanomaterial. Also, an accurate mathematical model was used to estimate the parameters of the PV panel, which is a non-linear optimization problem. Based on the experimental data and using the particle swarm optimization (PSO) algorithm, the optimal five parameters of a single diode model of a PV panel were determined in this study. This experimental work was conducted and carried out in the Renewable Energy Laboratory of Assiut University, Egypt. A comparative analysis was completed for three identical solar PV panels; the first panel was coated with hydrophobic SiO 2 nanomaterial, so it was considered to be a self-cleaning panel; the second panel was uncoated and cleaned manually on a daily basis; and the third panel was kept dusty all the time through the experimental investigation, and was used as a reference. Experimentally, the output power of the PV panels was monitored for each panel in this study. Also, the anti-static and anti-reflection effects of coating solar PV panels with hydrophobic SiO 2 nanomaterial were investigated experimentally. According to the obtained experimental results, it was found that the use of SiO 2 coating for PV panels results in the better performance of the PV panels. The overall efficiency of the coated panel increased by 15% and 5%, compared to the dusty panel and the uncoated panel which was manually cleaned daily, respectively.

In winter, the main cause of the reduction in power output of photovoltaic (PV) systems is low solar radiation and snowfall. Since PV system characteristics and seasonal changes vary according to the location where the installation is made, efforts to determine the losses caused by snow cover on the PV system continue in many countries. In this study, a 10 kWp grid-connected PV solar power system was installed in Konya, Turkey. The PV system consists of a hybrid inverter and 36 PV modules in two strings. In order to determine the effects of snowfall, one of the plant's strings was continuously tried to be kept clean with a squeegee every half hour. The effectiveness of the clearing method has been evaluated in terms of labor force used and the clearing time. The power output losses were determined by comparing of the recorded power output of cleaned and uncleaned strings. The power loss caused by snowfall experienced on different days during January in 2017 was assessed on a daily and monthly basis, depending on the data obtained from the meteorological station (global radiation, air temperature, wind speed and relative humidity). The results of this study show that the system yield significantly decreases when the PV solar system is covered with snowfall. On the days when modules were completely covered with snow, the experienced energy loss was nearly 93%. When the power loss of the plant is evaluated on a monthly scale, the ratio between cleaned modules and snow covered modules was calculated as approximately 23%.

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Ice formation and accretion may hinder the operation of many systems critical to national infrastructure, including airplanes, power lines, windmills, ships, and telecommunications equipment. Yet despite the pervasiveness of the icing problem, the fundamentals of ice adhesion have received relatively little attention in the scientific literature and it is not widely understood which attributes must be tuned to systematically design "icephobic" surfaces that are resistant to icing. Here we probe the relationships between advancing/receding water contact angles and the strength of ice adhesion to bare steel and twenty-one different test coatings (∼200-300 nm thick) applied to the nominally smooth steel discs. Contact angles are measured using a commercially available goniometer, whereas the average strengths of ice adhesion are evaluated with a custom-built laboratory-scale adhesion apparatus. The coatings investigated comprise commercially available polymers and fluorinated polyhedral oligomeric silsesquioxane (fluorodecyl POSS), a low-surface-energy additive known to enhance liquid repellency. Ice adhesion strength correlates strongly with the practical work of adhesion required to remove a liquid water drop from each test surface (i.e., with the quantity [1 + cos θ rec ]), and the average strength of ice adhesion was reduced by as much as a factor of 4.2 when bare steel discs were coated with fluorodecyl POSS-containing materials. We argue that any further appreciable reduction in ice adhesion strength will require textured surfaces, as no known materials exhibit receding water contact angles on smooth/flat surfaces that are significantly above those reported here (i.e., the values of [1 + cos θ rec ] reported here have essentially reached a minimum for known materials).

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

This study aims at analyzing the hydrophobicity of a wide range of fluorine-carbon coatings, from carbon-rich to fluorine-rich materials, carried out within the framework of a search for snow-and ice-repellent materials. A simple model accounting for both surface chemical composition and roughness was applied to evaluate the wettability of various fluorocarbon coatings. Apparent contact angles of water on both smooth and rough surfaces are modeled as functions of the fluorination degree and area fraction of air entrapped into the water-solid interface. It is shown that low-fluorinated C-F coatings are hardly hydrophobic and likely will not provide snow or ice-repellent properties. At the same time, coatings with higher F concentration demonstrate good hydrophobicity, which can be further enhanced by air entrapment into rough surface structures. Thus, as coatings with good mechanical properties, low surface energy and surface roughness governed by deposition parameters, such C-F materials may be of potential use for snow-and ice-repellent purposes. r

The analysis of wetting behavior of self-assembled monolayers of alkylsilanes is presented. A simple model accounting for various surface fractions of CH 3 and CH 2 groups (self-assembly order/disorder) is used. The effect of inclusion of air in the structure of rough silanized surfaces is also considered. The importance of reduced solid-water contact area and assembly order of organic monomers is demonstrated for achieving both high contact angle and low sliding angle. As coatings with low surface energy, these materials may be of potential use for ice-repellent purposes. #

This article studies the anti-ice performance of several micro/nano-rough hydrophobic coatings with different surface chemistry and topography. The coatings were prepared by spin-coating or dip coating and used organosilane, fluoropolymer or silicone rubber as a top layer. Artificially created glaze ice, similar to the naturally accreted one, was deposited on the nanostructured surfaces by spraying supercooled water microdroplets (average size ˜80 μm) in a wind tunnel at subzero temperature (-10 °C). The ice adhesion strength was evaluated by spinning the samples in a centrifuge at constantly increasing speed until ice delamination occurred. The results show that the anti-icing properties of the tested materials deteriorate, as their surface asperities seem to be gradually broken during icing/de-icing cycles. Therefore, the durability of anti-icing properties appears to be an important point for further research. It is also shown that the anti-icing efficiency of the tested superhydrophobic surfaces is significantly lower in a humid atmosphere, as water condensation both on top and between surface asperities takes place, leading to high values of ice adhesion strength. This implies that superhydrophobic surfaces may not always be ice-phobic in the presence of humidity, which can limit their wide use as anti-icing materials.

In this work, ice repellency of rough hydrophobic coatings based on different materials and with different surface topographies is evaluated. The coatings were prepared either from a fluoropolymer incorporated with nanoparticles or by etching aluminum alloy substrate followed by further hydrophobization of the rough surface via an organosilane monolayer adsorbed from solution. This allowed comparing the icereleasing performance of rough surfaces with high water contact angles (∼ 150-153°) and different dynamic hydrophobicities and mechanical properties. Artificially created glaze ice, similar to naturally occurring glaze, was accreted on the surfaces by spraying supercooled water microdroplets in a wind tunnel at subzero temperature. The ice adhesion strength was evaluated by spinning the samples in a centrifuge at constantly increasing speeds until ice detachment occurred. The results showed that, after several icing-deicing cycles, the more robust surfaces prepared by etching the aluminum substrate maintained their ice-releasing properties better, compared to their counterparts based on nanoparticle-incorporated fluoropolymer. The effect of the dynamic hydrophobicity of the coatings was also examined, clearly demonstrating that the surface with low dynamic hydrophobicity is not ice-repellent, although it demonstrates large values of water contact angle.

In this work, we measured the adhesion strength of artificially created glaze ice (similar to accreted in nature) on rough fluoropolymer-based hydrophobic surfaces with different contact angle (CA) and wetting hysteresis. The previously reported direct correlation between ice repellency and CA on superhydrophobic surfaces is shown to be only valid for surfaces with low wetting hysteresis. Another correlation was found between wetting hysteresis and ice adhesion strength on rough surfaces with similar chemistry.

This article studies the anti-ice performance of several micro/nano-rough hydrophobic coatings with different surface chemistry and topography. The coatings were prepared by spin-coating or dip coating and used organosilane, fluoropolymer or silicone rubber as a top layer. Artificially created glaze ice, similar to the naturally accreted one, was deposited on the nanostructured surfaces by spraying supercooled water microdroplets (average size ∼80 m) in a wind tunnel at subzero temperature (−10 • C). The ice adhesion strength was evaluated by spinning the samples in a centrifuge at constantly increasing speed until ice delamination occurred. The results show that the anti-icing properties of the tested materials deteriorate, as their surface asperities seem to be gradually broken during icing/de-icing cycles. Therefore, the durability of anti-icing properties appears to be an important point for further research. It is also shown that the anti-icing efficiency of the tested superhydrophobic surfaces is significantly lower in a humid atmosphere, as water condensation both on top and between surface asperities takes place, leading to high values of ice adhesion strength. This implies that superhydrophobic surfaces may not always be ice-phobic in the presence of humidity, which can limit their wide use as anti-icing materials.

This study aims at analyzing the hydrophobicity of a wide range of fluorine-carbon coatings, from carbon-rich to fluorine-rich materials, carried out within the framework of a search for snow-and ice-repellent materials. A simple model accounting for both surface chemical composition and roughness was applied to evaluate the wettability of various fluorocarbon coatings. Apparent contact angles of water on both smooth and rough surfaces are modeled as functions of the fluorination degree and area fraction of air entrapped into the water-solid interface. It is shown that low-fluorinated C-F coatings are hardly hydrophobic and likely will not provide snow or ice-repellent properties. At the same time, coatings with higher F concentration demonstrate good hydrophobicity, which can be further enhanced by air entrapment into rough surface structures. Thus, as coatings with good mechanical properties, low surface energy and surface roughness governed by deposition parameters, such C-F materials may be of potential use for snow-and ice-repellent purposes. r

In this study, room-temperature vulcanized silicone rubber coatings were prepared by spin-coating hexane-diluted suspensions onto aluminum substrates. Various amounts of carbon-black, titania or ceria nanopowders were incorporated to the coatings as dopants in order to modify their surface roughness, hydrophobic and electrical properties. By changing deposition parameters, superhydrophobic surfaces could be prepared. The freezing behavior of small water droplets was investigated on nanostructured composite surfaces exhibiting different values of wetting hysteresis and was compared with that on uncoated polished aluminum. At approximately −15 • C, the water droplets were found to freeze on polished aluminum after approximately 5 s, while their freezing was delayed to as long as ∼12-13 min on superhydrophobic nanocomposite surfaces doped with ceria or titania powders. Correlations between the wetting hysteresis (and surface roughness) of the samples and freezing time of water droplets on their surfaces were also observed. Icing tests demonstrated delayed ice formation and lower adhesion strength on superhydrophobic samples with small wetting hysteresis.

This work investigates the anti-ice performance of various superhydrophobic surfaces under different conditions. The adhesion strength of glaze ice (similar to that deposited during "freezing rain") is used as a measure of ice-releasing properties. The results show that the ice-repellent properties of the materials deteriorate during icing/deicing cycles, as surface asperities appear to be gradually damaged. It is also shown that the anti-icing efficiency of superhydrophobic surfaces is significantly lower in a humid atmosphere, as water condensation both on top of and between surface asperities takes place, leading to significantly larger values of ice adhesion strength. This work thus shows that superhydrophobic surfaces are not always ice-repellent and their use as anti-ice materials may therefore be limited.

Solar photovoltaic (PV) systems are frequently installed in climates with significant snowfall. To better understand the effects of snowfall on the performance of PV systems, a multi-angle, multi-technology PV system was commissioned and monitored over two winters. A novel methodology was introduced and validated with this system, which allows for the determination of snowfall losses from time-series performance data with correlated meteorological observations down to a 5-min resolution. In addition, a new method for determining the probability distribution of snow deposition on a module from image data was developed. It was found that the losses due to snowfall are dependent on the angle and technology being considered and the effects of increased albedo in the surroundings of a PV system can increase expected yields, particularly in the case of high tilt angle systems. Existing methods for predicting losses due to snowfall were investigated, and were found to provide overly conservative estimates of snow losses. Overall the results show that the proper assessment of snow related losses can help improve system performance and maintenance. It is concluded that proper characterization of the snowfall effect on PV system performance can influence better systems optimization for climates experiencing snowfall.

In this paper the theory of mismatch factor of the terrestrial solar spectrum is extended to more accurately represent the irradiance available to specific types of photovoltaic (PV) materials from surface albedo. Using spectra derived from the SBDART radiative transfer model, the effects of a spectrally responsive albedo are illustrated by the differences between the spectrally weighted albedo of hydrogenated amorphous silicon (a-Si:H) and crystalline silicon (c-Si)-based PV technology compared to traditional spectral-integrated albedo predictions. In order to quantify this effect for varying representative spectra, spectra for cloud optical thicknesses from 0 to 150 were investigated. Grass was found to be a poor reflector in the a-Si:H spectral response range with a spectrally responsive albedo of 0.08–0.07 as cloud optical thickness increased from 0 to 150, whereas snow is a good reflector with a relatively constant spectrally responsive albedo of 0.94 as cloud optical thickness increased from 0 to 150. For c-Si PV systems the spectrally responsive albedo for snow and grass were found to be 0.87–0.91 and 0.24–0.15 as cloud optical thickness increased from 0 to 150, respectively. These values can be compared to the albedo typically predicted from spectral integration relative to an AM1.5 spectrum of 0.78 for snow and 0.23 for grass. On a yearly basis, the use of improper albedo values can lead to an under-prediction of system yields of 0.04%, 2.4%, and 10.5% for systems at 25°, 40°, and 90° tilt from the horizontal, respectively. This can lead to improper PV systems optimization and masking of other loss mechanisms. The results of this study have implications for both solar systems evaluation and systems design, and supports further research on tailoring PV technologies specifically to the climate and geographic location where they are deployed.

As solar photovoltaic (PV) installations have become more common in regions that experience substantial snowfall, losses in energy production due to snow coverage have grown in concern. Several post-production surface coatings have been proposed to enhance snow shedding to reduce these snow related losses. In this paper, a novel methodology is developed to determine the effectiveness of a snow clearing from a PV module and is used to evaluate the snow shedding effectiveness of any module surface treatment. Measured PV output is compared to modeled PV output in a generalizable method that allows for the determination of the length of time a panel is covered with snowfall using electrical performance data. This model accounts for module degradation during long-term outdoor testing and other external factors effecting performance, such as persistent soiling losses. This methodology was tested on modules that had one of four hydrodyanmic surface coatings, as well as one module with a prismatic glass front in order to determine the snow clearing effectiveness of these surfaces as compared to conventional plain glass. The methodology was validated, but the surface coatings tested did not have an appreciable positive effect on snow clearance, and in some cases tended to impede the shedding of snow. The physical mechanisms responsible for the results are discussed.

The accurate prediction of yields from photovoltaic systems (PV) is critical for their proper operation and financing, and in northern latitudes the effects of snowfall on yield can become significant. This work provides methods for identifying snowfall effects from commonly collected performance data, and recommends a model to allow for prediction of these effects based solely on meteorological time series. The model was validated with data from two large-scale (>8MW) operational PV plants. For the low tilt angles most affected by snowfall, this analysis was able to accurately predict both daily and mean values of snow effects. This methodology will enable system operators to utilize performance data to accurately identify and predict snowfall losses, and will assist system designers to optimize for the effects of snowfall on new system designs.

Correct modelling of solar photovoltaic (PV) system yields is necessary to optimize system design, improve reliability of projected outputs to ensure favourable project financing and to facilitate proper operations and maintenance. An improved methodology for fine resolution modelling of PV systems is presented using module short-circuit current (Isc) at 5-min time-scales, and clearly identifies pertinent error mechanisms that arise when working at this high resolution. This work used a modified version of the Sandia array performance model, and introduces new factors to the calculation of Isc to account for identified error mechanisms, including instrumentation alignment, spectral, and module power tolerance errors. A simple methodology was introduced and verified where specific module parameters can be derived solely from properly filtered performance time series data. In particular, this paper focused on methodologies for determining the predicted Isc for a variety of solar PV module types. These methods of regressive analysis significantly reduced the error of the predicted model, and demonstrate the need for this form of modelling when evaluating long term PV array performance. This methodology has applications for current systems operators, which will enable the extraction of useful module parameters from existing data in addition to more precise continuous monitoring of existing systems, and can also be used to more accurately model and optimize new systems.