A Method to Extract Soiling Loss Data from Soiling Stations with Imperfect Cleaning Schedules

Muller, Matthew; Micheli, Leonardo; Martinez‐Morales, Alfredo A.

doi:10.1109/pvsc.2017.8366214

Cited by 20 publications

(13 citation statements)

References 6 publications

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“…Generally, two PV cells or modules are employed: one of them is regularly cleaned (control device), whereas the second is left to soil naturally (soiled device) [13]. Despite the simple approach, these stations require regular cleanings, which might be expensive to perform, in order to limit the uncertainty of the measurement, as the error associated with stations that are not well maintained can be as high as the soiling loss [14].…”

Section: Introductionmentioning

confidence: 99%

Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements

Micheli

Caballero

Férnández

et al. 2019

Energy

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This paper presents the results of an investigation on the spectral losses of photovoltaic (PV) soiling. The transmittance of a glass coupon exposed to natural soiling outdoors in Jaén, southern Spain, has been measured weekly and used to estimate the soiling losses that various types of photovoltaic materials would experience if installed in the same location. The results suggest that measuring the hemispherical transmittance of the soiling accumulated on a PV glass coupon can give enough information to quantify the impact of soiling on energy production. Each PV technology is found to have a preferred spectral region, or a specific single wavelength, for which the transmittance through a PV glass coupon could be used for the best estimation of soiling losses. Overall, considering the average spectral transmittance between the extreme wavelengths of the material-specific absorption band, or the transmittance of soiling at a single wavelength between 500 and 600 nm yields the best estimations for different PV technologies. The results of this work can lead to innovative approaches to detect soiling in the field and to estimate the impact of spectral changes induced by soiling on PV energy production.

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Section: Introductionmentioning

confidence: 99%

Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements

Micheli

Caballero

Férnández

et al. 2019

Energy

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“…Soiling stations have the advantage of directly measuring the impact of soiling on PV, but require careful maintenance to avoid significant measurement errors 8 . Novel sensors that require less maintenance and do not need a clean reference PV device are getting the attention of the market, and are based on the optical characterization of a soiled glass coupon 9,10 .…”

mentioning

confidence: 99%

Modelling photovoltaic soiling losses through optical characterization

Smestad

Germer

Alrashidi

et al. 2020

Sci Rep

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The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (iR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the Ångström turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of pV modules from the particle size distribution and the cleanliness value. Soiling has a negative impact on the economic revenues of PV installations, not only because it reduces the amount of energy converted by the PV modules, but also because it introduces additional operating and maintenance costs and, at the same time, increases the uncertainty on the estimation of PV performance, leading to both higher financial risks and interest rates charged to plant developers. Power reductions greater than 50% have been reported in the literature because of soiling 1,2 ; it has been estimated that an average loss of 4% on the global annual energy yield of PV could cause losses in revenue on the order of 2 × 10 9 US$ annually 3. A careful monitoring of soiling is required to mitigate its effect 4. Soiling losses are generally quantified by using soiling stations. These systems are made of at least two PV devices, one of which is regularly cleaned while the other is left to soil naturally. By comparing the ratio of the electrical outputs of the two devices, it is possible to estimate the impact of soiling on the PV performance 5,6. The International Electrotechnical Commission's (IEC) metric to monitor and quantify the impact of soiling on PV modules is the soiling ratio, r s , which expresses the ratio of the electrical output of a soiled PV device to the output of the same device under clean conditions 7. Like the transmittance, a higher soiling ratio translates to less soiling deposited on the modules. A value of 1 indicates clean conditions, with no soiling. For a more detailed definition of r s , please refer to the Methodology section. The fractional loss of solar-generated power due to soiling is 1 − r s .

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“…Likewise, an analysis done in Chile suggests that, an optimal cleaning program can be designed from a robust statistical representation of power generation characteristics of a PV plant using correlation of the obtained data from the real PV plant to that of ideal power based on geographical data. In an experiment performed in the Southwestern United States [83], cleaning schedule is optimized as shown in Figure 11 by using the Soiling Ratio (Sr): defined by the ratio of the short circuit currents of naturally soiling PV cell to that of intended to be cleaned cell. Along with the use of precipitation data, the corrected Sr ratio over the time is drawn as per the changes obtained in the consecutive instances.…”

Section: A Scheduling Of Solar Panel Cleaning Interventionmentioning

confidence: 99%

“…The more the parameters included, the more accurate the model might be and consequently, the more sophisticated and costly the system shall be. In the study performed at middle east countries [80,83], due to different soiling conditions at different months of a year, it is experienced that seasonal meteorological data have paramount importance in determining the cleaning intervention. (ii) Data Processing: Before casting the calibration model for the decision making, it is necessary to process the data collected which seems to be erroneous.…”

Section: B Suggested Schematics For Machine Learning Implementation mentioning

confidence: 99%

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

et al. 2020

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Solar Photovoltaic System (SPV) is one of the growing green energy sources having immense penetration in the national grid as well as the off-grid around the globe. Regardless of different solar insolation level at various regions of the world, SPV performance is also affected by several factors: conversion efficiency of PV cell technology, ambient temperature and humidity, soiling and seasonal/weather patterns. The rise in PV cell temperature and soiling is found to be detrimental issues regarding power plant performance and life expectancy leading alterations in the levelised cost of energy (LCoE). In this paper, authors present a short glance about factors affecting the performance of photovoltaic modules and re-discuss their usability in cleaning intervention decision-making models. With some highlights on the essence of cleaning to mitigate the soiling issues in PV power plants, this paper presents the existing cleaning techniques and practices along with their evaluations. The need for an optimal cleaning intervention by using advanced scientific tools rather than by visual inspection is drawing the attention of PV experts. The authors finally suggest a schematic of a decision-making model which involves the use of probable parameters, data processing techniques and machine learning tools. The implementation of data science and machine learning in a solar PV panel cleaning system could be a remarkable advancement in the field of renewable energy.

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A Method to Extract Soiling Loss Data from Soiling Stations with Imperfect Cleaning Schedules

Cited by 20 publications

References 6 publications

Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements

Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements

Modelling photovoltaic soiling losses through optical characterization

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

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