In terawatt-scale photovoltaic (PV) deployment, managing the impact of PV soiling is vital as it reduces energy output. Accurately quantifying soiling losses from generation data is a key step in optimising cleaning of PV sys tems. The Stochastic Rate and Recovery (SRR) model detects PV cleaning events by comparing daily performance ratio (PR) values with a 14-day centered moving median. Events are identified when the difference exceeds a threshold of Q3 + ax (Q3-Q1). Traditionally, the SRR model uses a fixed a value of 1.5. However, fixed parameters perform poorly for utility-scale systems, and manual tuning of the window length and a is not scalable. In this work, we introduce a fully automated framework that varies the moving-median window length and a over a defined range and selects the optimal combination using the F1-score. This removes the need for manual tuning. Additionally, a rainfall-threshold sensitivity analysis is presented that identifies the minimum daily rainfall required for partial cleaning, improving rain-driven cleaning detection. The methodology was tested on rooftop and ground-mounted systems in India and the U.S., including cases using satellite-based weather data. During dry periods, the method accurately detected cleaning events, achieving a 78% recall and 87.4% F1-score at a utility-scale plant. Despite reducing uncertainty, limitations remain during the monsoon season, when rapid irradiance changes lead to false detections. Partial cleanings from wind were not considered due to unreliable wind and humidity data. Future work will address these issues. Overall, the framework supports scalable soiling analysis and informed operational decision-making.

Tunnel oxide passivated contact (TOPCon) is the most widely used technology for fabricating high-efficiency silicon solar cells (SCs). This technology has been successfully scaled up and is being produced commercially by major photovoltaic (PV) manufacturers worldwide. For most commercial applications, polysilicon (poly-Si) based passivating contacts on the rear side are being used. However, the current SCs can be improved further if the recombination losses on the front side emitter and under the metal contacts can be either eliminated or minimized. Furthermore, it is essential to comprehend the various fundamental mechanisms and factors that contribute to achieving higher efficiencies. This work examines the performance of various TOPCon device structures, considering both monofacial and bifacial configurations with single-side (SS) and double-side (DS) TOPCon structures. TOPCon structures based on doped poly-Si are explored and modelled through Sentaurus TCAD software. An advanced selective-emitter (SE)- based bifacial DS-TOPCon cell is proposed and analyzed to reduce parasitic absorption and contact resistivity, thereby improving current collection efficiency. By following the SE technology, the optimized device achieved an efficiency of up to 26.7%. We believe this study provides an understanding of the excellent performance of TOPCon devices and could provide a pathway for improving the performance of advanced commercial TOPCon SCs.

Soiling significantly reduces the performance of PV modules, and robotic cleaning is commonly used to mitigate dust in large PV plants. However, frequent cleaning can damage antireflective/antisoiling coatings. This paper examines factors affecting abrasion damage on four commercial hydrophobic coatings (A, B, C and D) using a cleaning cycles simulator that emulates real-world conditions. Key findings include: (1) the presence of dust/soil during cleaning cycles decreases the coating life by 82 & times;, compared to only-clean cycles, thus acting as the most significant stressor that abrades the coated samples. (2) Cleaning the samples with a brush material that has higher hardness (1.3 & times;), coated samples show 3 & times; lesser coating life. (3) Microfibre cloth brush causes the least damage to the antisoiling coatings (compared to other brush materials), as it applies the smallest weight on the sample's surface. (4) Coated samples show reduced (3 & times;) coating life when cleaned with the direction of brush rotation as 'towards the direction of travel,' compared to 'opposite to the direction of travel' and 'clockwise rotation.' When the brush rotates towards the direction of travel, greater abrasion damage is seen due to the combined effect of dust particles (being dragged along with the brush) and brush bristles. (5) Reducing the horizontal velocity of brush travel by four times from 0.4 to 0.1 m/s results in a 3 & times; decrease in coating life. PV developers and robotic cleaning manufacturers should optimize these variables to minimize damage to the top coatings applied on PV modules.

If not cleaned, energy loss due to soiling can go up to 50% in 4 months in parts of India. Such losses significantly impact the economic viability of PV deployments in sunbelt countries like the Middle East and India. Antisoiling coating offers a cost-effective dust mitigation strategy. However, as the coating is applied on the outer surface of the PV module, it has to endure various environmental stressors. Our previous studies have shown that rain is one of the most significant stressors that degrade the antisoiling coatings. This study estimates the lifetime of antisoiling coatings when subjected to rain, considering the pH of the rainwater as the stressor. The study was done on four commercial hydrophobic antisoiling coatings (A-D). Coated samples were exposed to water immersion tests; thus, the effect of the impact of the raindrop is not considered in this study. Characteristic times to failure were estimated using the Weibull distribution. The activation energy and pH dependence factor (N) were calculated using the Arrhenius-modified Pecks model. The activation energy of Coatings A-D were calculated to be 0.09, 0.43, 0.09, and 0.56 eV, respectively. Positive activation energy indicated that the coating life decreases with increased temperature. The pH dependence factor (N) for Coatings A-D was estimated as 3.6, 1.4, 0.28, and 1.31, respectively. Based on the modelled equation, we predict the life of the coatings based on Miner's rule at two different locations. All coatings showed lower coating life irrespective of the pH when exposed at a tilt angle lower than their respective roll-off angle. PV plant developers can use these data to predict coating life against this stressor at different locations, which can help identify coatings that work for specific weather conditions. It may also act as the starting point to model the effect of the combination of stressors.

The durability of anti-soiling coatings (ASC) on photovoltaic (PV) modules is critical for mitigating soiling-induced energy losses and reducing cleaning frequency, particularly in regions with high soiling rates. This study experimentally evaluates the impact of four cleaning loads (300 gf, 600 gf, 900 gf, and 1010 gf) on hydrophobic ASC durability using glass coupons in a controlled indoor environment. To replicate realistic field conditions, a dew-dust-dry-clean (DDDC) test sequence was employed, simulating daily temperature, humidity and soiling variations observed in actual PV installations. To assess cleaning efficacy, the indoor experiment was conducted for two artificial dust gravimetric densities (0.2 and 0.6 mg/cm2). The findings reveal that ASC durability is not linearly dependent on the applied load but varies based on the interaction between the brush bristles and the glass surface. ASC subjected to lower loads exhibited extended durability, while moderate loads accelerated degradation due to increased bristle contact. Interestingly, the highest load showed improved durability compared to moderate loads. The number of cleaning cycles required for complete soiling removal decreases with increasing loads. For identical cleaning efficacy, the durability of the coating is marginally worse for the highest load than for the smallest load, while the coating degraded the fastest for moderate loads. These results provide critical insights for optimizing cleaning strategies to balance coating durability and cleaning efficacy, ensuring the long-term performance and cost-effectiveness of anti-soiling and anti-reflective coatings.

Soiling significantly impacts the efficiency of photovoltaic (PV) systems, especially in regions with heavy dust deposition like India. The issue is exacerbated by spatially non-uniform soiling in utility-scale PV plants, where certain areas of the plant experience higher losses than others, complicating maintenance efforts. In this study, we analysed string-level SCADA data from a 50 MWp utility-scale PV plant in South India divided into several zones to create detailed soiling maps. Using these maps, we developed both string-optimized and zone- optimized cleaning methodologies. The string-optimized approach utilized four specific cleaning thresholds to help determine the most profitable cleaning areas in each zone, while the zone-optimized approach aimed to streamline cleaning processes, enhance PV plant performance, and resource efficiency. Additionally, unlike previous studies, this analysis accounted for DC cabling losses, further refining the evaluation of soiling impact. The results in-terms of cleaning profit generated were compared with the same made by actual logged cleaning. Additionally, we performed a sensitivity analysis by varying solar PV electricity tariffs and cleaning costs to evaluate the economic viability of different cleaning strategies. The analysis indicated that the 85% cleaning threshold is the most economical, particularly as PV electricity prices continue to decline. Our findings suggest that structured cleaning schedules based on soiling data can significantly improve PV plant performance and profitability. This approach can be replicated in similar PV plants to support India's growing PV sector, ultimately helping the country become a global leader in solar energy.

We investigated the impact of pyranometer soiling on the accuracy of PV module performance ratio (PR) evaluation and also propose methods to optimize cleaning intervals for pyranometers. By comparing the soiling rates of the pyranometer and PV module at a site in Mumbai, India, we found that pyranometer soiling rates were significantly lower, ranging from 36 % to 48 % of PV module soiling rates. Soiling of pyranometers led to an underestimation of module soiling rates from PR by 30-43 %, causing delayed detection of optimal cleaning intervals. This delay resulted in substantial energy and revenue losses, as illustrated by a 100 MW PV plant case study. To address this, a daily irradiance uncertainty threshold of 1 % was established, aligning with industry standards, to optimize pyranometer cleaning frequency. The optimal cleaning interval was determined to be 3-4 days for Mumbai. We also recommend pyranometer cleaning intervals for 103 global locations, considering local soiling rates. Furthermore, we analyzed the diurnal variation in pyranometer soiling. We found that temperature-corrected PR during early morning and late afternoon hours reduces errors caused by pyranometer soiling. The proposed methodology provides practical guidelines to mitigate the effects of pyranometer soiling, enhancing the reliability of PV performance evaluation and optimizing maintenance scheduling.

Aerial Infrared thermography has recently emerged as an industry-standard technique for 100% inspection of large utility scale Photovoltaic (PV) power plants due to its high throughput and low cost. However, since the performance warranty definitions are tied to the electrical (and not thermal) performance of the modules, this paper investigates the accuracy of defect detection and classification from aerial thermography of a 10 MW PV power plant using Electroluminescence (EL) and Current-Voltage (I-V) measurements from the field and the applicability of classification for warranty claims. Analysis of the root cause of certain misclassifications of defects due to similar thermal signatures from different PV degradation mechanisms is also presented. It is shown that the aerial infrared thermography had high specificity (∼90%) for this PV plant affected by Potential Induced Degradation (PID). Insights for reducing the False Positives and False Negatives are discussed and the detailed module by module electrical performance and EL data for these cases is provided in supplementary material. A methodology for identifying modules eligible for warranty claims from the aerial infrared thermography is also presented.

Photoluminescence (PL) and electroluminescence (EL) imaging techniques have been widely used for the performance analysis and diagnosis of different types of solar cells. Contacted measurements of solar cells have become increasingly challenging in emerging cell designs with a large number of narrow or no busbars. Contactless EL measurements by themselves have attracted attention in the recent literature.In this work, we report preliminary work on a contactless EL measurement setup for the diagnosis of solar cells. Contactless EL imaging is demonstrated and compared with PL and contacted-EL images. Defects seen in the PL and contacted EL images are also seen in the contactless EL images, demonstrating the possibilities of the new system.

Tunnel oxide passivated contact (TOPCon) is an emerging technology for highly efficient with excellent passivation photovoltaic (PV) devices. However, a standard TOPCon cell suffers from insufficient efficiency gain and recombination losses due to the presence of a direct metal-crystalline silicon contact. Therefore, bifacial configurations with advanced passivated structures have been considered in this research work. In this work, the performance of bifacial TOPCon device with double-side (DS) TOPCon structures, integrated with poly-Si, is explored and modeled using Sentaurus TCAD software. The reported study develops a framework to obtain state-of-the-art bifacial TOPCon structures with optimized input parameters and considering tunneling structures. The impact of collective front/rear SiNx layer thickness (from 50 to 100 nm) and p+ poly Si thickness (from 20 to 100 nm) on the performance of bifacial-DS TOPCon solar is studied and analyzed for optimized PV performance. This research study reveals that optimizing the p+ poly-Si thickness enhances carrier collection with minimal bulk recombination losses. The PV performance of DS structure indicates that incorporating DS carrier selective contacts increases the PV efficiency. Also, the detailed analysis of bifacial TOPCon structure reveals that suppressing the recombination by incorporating tunneling structures on both sides can be the key strategy to improve PV performance with an optimized efficiency of 26.3%. The reported study set a clear direction for higher PV performance by incorporating a tunneling approach in next-generation c-Si solar cells at low cost.

A longitudinal study of over 2300 degradation rate samples combined from All India Surveys of PV Reliability 2014, 2016 and 2018 is presented. The effect of the STC correction procedure on the mean / median of the distribution of the calculated degradation rate is investigated. It is also demonstrated conclusively that the modules in hot climates degrade at a significantly higher median rate as compared to the modules in non-hot climates. The paper also provides insights for interpreting the time evolution of the calculated degradation rate distributions from various field surveys using a modified nameplate based methodology. It is also shown that broadening of the calculated degradation rate distribution in the subsequent surveys / emergence of a new peak in the distribution can be a signature of a new degradation mechanism affecting a subset of modules at a site.

In fabricating passivated emitter and rear contact (PERC) solar cells, creating small openings on the SiN x coated silicon substrate to establish metal contacts necessitates using a nanosecond pulsed green laser for ablation. However, the thermal nature of laser ablation poses a challenge. Excessive nitrogen diffusion from SiN x into the silicon substrate can compromise the electrical performance of solar cells. Therefore, this study aims to comprehensively understand how laser parameters impact the electrical properties of such solar cells. PERC solar cell precursors were initially fabricated by ablating the SiN x layer at four laser fluences, each corresponding to different regimes: solid, liquid, vapor, and phase explosion. Subsequently, various optical, chemical, and electrical characterizations were conducted on the solar cell precursors. Complete PERC solar cells were also fabricated to measure quantum efficiency (QE). In the solid regime, SiN x removal is precise, with minimal thermal damage, resulting in a nitrogen concentration of approximately 0.15%. Photoluminescence count and carrier lifetime are notably higher by 21% in the solid regime compared to the explosive regime. The QE measurement at 984 nm quantitatively assesses rear-side recombination losses. Notably, 26% of the area exhibits a 56% QE in the solid state, while this number drops to around 22% in the explosive state. This decline can be attributed to increased thermal damage in the explosive regime, which augments recombination centers and diminishes the solar cell performance. PERC solar cells ablated in the solid regime showcase lower subsurface damage, superior electrical characteristics, and higher performance than those ablated in the explosive regime.

Performance ratio (PR), is a widely used performance metric for PV powerplant, and it is reported to exhibit seasonal variations. Seasonal variation in PR can introduce errors in estimating system performance degradation due to component degradation and soiling from SCADA data. Partly to address seasonal fluctuations, temperature-corrected PR (TcorrPR) is widely used. This work introduces an empirical, Fourier series based signal processing approach to isolate seasonal variations in daily PR and TcorrPR. Comparing the degradation rates and soiling rates obtained after removing seasonal variation, it is demonstrated that the PR without temperature correction is adequate for degradation and soiling analysis. This opens up the possibility to diminish the reliance on ambient temperature and wind speed data, and temperature coefficient of power of PV modules, in the analysis of PV system performance.

The accumulation of dust and other particles on the photovoltaic (PV) modules leads to significant energy losses. In this paper, we show that the module temperature decreases with increasing accumulation of soil on the module glass surface in an open rack rooftop installation by analysing the output of two 327 W-p Sunpower PV modules over a period of two years from 2015 to 2017. Soiling rates were extracted from the daily energy time series data, performance ratio (PR) and temperature-corrected performance ratio. It was found that the soiling rate extracted from the PR is slightly less than that obtained from the time series of energy, whereas the temperature-corrected PR significantly overestimates the soiling rate. This can be explained from the fact that the temperature-corrected PR also corrects for the temperature difference resulting from soiling. Based on these results, we propose that the soiling rate estimated from PR is a better estimate of soiling than that obtained from temperature-corrected PR.

Nanosecond lasers can be used for the selective ablation of ultra-thin SiNx layer (80–100 nm) coated on a silicon substrate with applications in the fabrication of PERC solar cells. Experiments reveal that the ablation process can occur in different regimes with material removal in solid, liquid, vapor, and explosion forms. This study investigates the different regimes for single pulse ablation through a physics-based model developed using the finite volume method in Ansys Fluent. The model accounts for various physical phenomena, including laser heating, melting, thermal expansion, thermal decomposition, Marangoni convection, vaporization, plasma formation, melt expulsion, and phase explosion. Experiments were conducted using Nd: YVO4 laser (λ=532 nm, pulse duration = 50 ns), and the measured single pulse crater profiles are in excellent agreement with the model predictions over a broad range of laser fluence. Results show that the SiNx layer breaks due to the thermal expansion of the underlying silicon at lower fluences (3.05–3.35 J/cm2) in a very narrow range. Above this fluence range lies another narrow range (3.35–5.55 J/cm2) wherein the SiNx layer is thermally decomposed into silicon and nitrogen, and craters are formed due to Marangoni convection in the melt pool formed. Ablation due to vaporization occurs in a broad fluence range (5.55–8.4 J/cm2), causing deeper ablation depths and material deposition at the crater edge. At higher fluences (¿8.4 J/cm2), explosive boiling occurs due to homogeneous nucleation, resulting in much higher ablation depths (¿1.6μm), broader crater widths, and poor surface finish. This model provides an understanding of different material removal regimes in laser ablation of SiNx-coated silicon.

The use of image analysis has often been suggested as a practical way to monitor the soiling accumulated on the surfaces of solar energy conversion devices. Indeed, the deposited soiling particles can be counted and characterized to calculate the area they cover, and this area can be converted into an energy loss. However, several particle counting methodologies exist and can lead to dissimilar results. This work focuses on the role of thresholding, an essential step where particles are distinguished from a background based on the pixel brightness. Sixteen automatic thresholding methods are assessed using 13 200 micrographs of glass coupons soiled at nine locations globally. In low‐to‐intermediate soiling conditions, the “Triangle” method is found to return the minimum coefficient of variation and a mean deviation closer to zero. On the other hand, methods assuming a bimodal distribution of pixel brightness underestimate the area coverage. In addition, since soiling can be unevenly distributed over a surface, different loss estimations can be returned when the same image analysis process is used on different spots on a sample's surface. For these reasons, image analysis should be repeated at multiple locations on each investigated surface.

Hollow silica nanoparticles (HSNs) represent a compelling and versatile class of nanomaterials with a broad range of applications owing to their unique attributes. These nanoparticles, characterized by hollow silica shells, exhibit exceptional qualities that make them highly attractive in various scientific and technological fields. The synthesis of HSNs involves a multi-step procedure, with the soft template method playing a crucial role in producing monodisperse hollow silica nanoparticles. The subsequent coating is achieved through the dip coating method. The efficacy of the coating is demonstrated by a 115 degrees contact angle and 92% average transmittance achieved within the 350-900 nm range. This combination of properties is particularly noteworthy, indicating the suitability of HSNs for applications requiring superior surface characteristics. Scanning electron microscope (SEM) analysis unveils the morphology of these nanoparticles, revealing particle sizes around 120 nm. Transmission electron microscope (TEM) further confirms the formation of hollow silica nanoparticles, with the shell thickness varying between 25 and 35 nm. Optical properties are assessed via UV-visible spectrophotometry, while surface wettability is studied through contact angle/interface system analysis. A key application of the coated material is demonstrated through its superior cleaning efficiency and transmittance compared to uncoated glass. This finding suggests potential applications in coating the cover glass of solar photovoltaic modules, where enhanced transmittance can positively impact energy conversion efficiency. Combining unique properties, efficient synthesis methods, and promising applications makes hollow silica nanoparticles a captivating area of research and development with far-reaching implications across various industries.

External contamination (“soiling”) of the incident surface is a major limiting factor for solar technologies. A 5-year field glass coupon study was conducted to better understand external contamination and its effects; compare cleaning methods and the use of preventative coatings; and explore the abrasion resulting from cleaning to advise on accelerated abrasion testing. Test sites included the cities of Dubai (UAE), Kuwait City (Kuwait), Mesa (AZ), Mumbai (India), and Sacramento (CA). Through the 5-year cumulative study, dry brush, water spray, and wet sponge and squeegee cleaning methods were compared to no cleaning. Optical microscopy was used to obtain images, including representative color images, grayscale images for object analysis, and oblique images for coating integrity assessment. A thresholding protocol was developed to analyze and distinguish specimens using the ImageJ software. Optical performance was quantified using a spectrophotometer, including comprehensive optical characterization (transmittance, reflectance, and absorptance in addition to forward- and back-scattering). Atomic force microscopy was used to verify the abrasion damage morphology, including the width and depth of surface scratches. Analysis of the results included correlation of optical performance and particle area coverage, rank order (by coating or location), and the acceleration factor for abrasion damage. The efficacy of external cleaning was more readily distinguished from the effectiveness of antisoiling coatings. The acceleration factor for dry brush cleaning of a porous silica coating was found to be on the order of unity.

Soiling on photovoltaic (PV) systems reduces operating efficiency, requiring frequent cleaning and maintenance. To quantify soiling loss directly from the PV performance time-series data, an essential step is to detect cleaning events accurately. However, analyzing the time-series data of utility-scale PV plants comes with its own set of challenges. Sensors not calibrated or maintained correctly, missing or inconsistent time stamps, defective components (PV modules or inverters), and incomplete maintenance logs can affect the process. Due to these reasons, daily variations in the PV performance ratio time-series data tend to be high, making it noisy. To overcome the above issue, a modified 1-sigma Hampel filter is used in this work, which aims to remove noise from the PV performance time-series data. The Hampel filter uses a rolling window and median absolute deviation to detect outliers in time-series data. Compared with the recent state-of-the-art filters used in time-series analysis, the proposed filter can effectively deal with noise with a Gaussian or non-Gaussian distribution, is easy to implement, and is resilient to local outliers in the data. The filter is applied to three PV datasets of different capacities to detect cleaning events accurately. In addition, using SCADA data analysis, we also discuss the role of wind speed and relative humidity in PV modules' cleaning, which was reported to result in panel cleaning under specific controlled experiments. Through the comprehensive analysis of SCADA data and accompanying metainformation, we show that our algorithm can detect cleaning under high wind speed and low relative humidity.

Soiling on Photovoltaic (PV) systems have become a serious issue worldwide. With time, the capacity of large-scale PV power plants is expected to rise, thereby making the issue of soiling more serious. One of the most common problems faced by PV power-plants today is the issue of non-uniform soiling, which refers to the uneven deposition of dust particles resulting in spatial variation of PV power across the plant. In this study, we analyze the soiling non-uniformity of a 2MW p installation located in South India. It is found that non-uniform soiling patterns, notably high losses occurs on PV strings near an unpaved road. This suggests the possibility of cleaning strings experiencing high soiling more frequently rather than cleaning the whole plant. This will enhance the overall efficiency and revenue generation in future maintenance strategies for large-scale solar installations.