Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI)

Alquthami, Thamer; Menoufi, Karim

doi:10.3390/su11174697

Cited by 23 publications

(12 citation statements)

References 34 publications

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“…Specifically, this phenomenon appears by covering the photovoltaic panels with a layer of dirt (causing deposition on the panels [13]), reducing the performance, and, consequently, the electrical production of each photovoltaic panel [14,15]. Therefore, soiling becomes a fundamental issue in terms of optimization [16] and, thus, on photovoltaic plant production. The accumulation of soiling and dirt makes it more difficult for radiation to reach the panels, thus reducing performance [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

2021

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Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).

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Section: Introductionmentioning

confidence: 99%

“…The studies looking at this topic have presented multiple conclusions, each related to the conditions occurring at the place and time that each study was carried out [16]. They have studied the importance of the soiling molecule size as well as the optimal inclination angle of the photovoltaic panels.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

2021

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“…The results confirmed those of previous studies, in which the I sc and P max outputs of the PV modules significantly decreased due to dust deposition exposure, while the change in the V oc and fill factor FF values of the modules were negligible. Alquthami and Menoufi [27] placed two panels in two different Egyptian cities over three weeks without cleaning. The electrical parameters of both modules were obtained indoors and under controlled temperature and illumination conditions using an I-V source meter.…”

Section: Introductionmentioning

confidence: 99%

Design of a Low-Cost Multiplexer for the Study of the Impact of Soiling on PV Panel Performance

et al. 2021

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Atmospheric factors, such as clouds, wind, dust, or aerosols, play an important role in the power generation of photovoltaic (PV) plants. Among these factors, soiling has been revealed as one of the most relevant causes diminishing the PV yield, mainly in arid zones or deserts. The effect of soiling on the PV performance can be analyzed by means of I–V curves measured simultaneously on two PV panels: one soiled and the other clean. To this end, two I–V tracers, or one I–V tracer along with a multiplexer, are needed. Unfortunately, these options are usually expensive, and only one I–V tracer is typically available at the site of interest. In this work, the design of a low-cost multiplexer is described. The multiplexer is controlled by a low-cost single-board microcontroller manufactured by ArduinoTM, and is capable of managing several pairs of PV panels almost simultaneously. The multiplexer can be installed outdoors, in contrast to many commercial I–V tracers or multiplexers. This advantage allows the soiling effect to be monitored on two PV panels, by means of I–V indoor tracers. I–V curves measured by the low-cost multiplexer are also presented, and preliminary results are analyzed.

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“…The annual monetary loss was estimated between 3 $/kW p to 70 $/kW p depending on the location [7]. In [8], the photovoltaic soiling index is addressed in order to assess the impact of dust on the performance of PV generation. This index considers annual dust accumulation patterns.…”

mentioning

confidence: 99%

“…In [7], soiling maps along Chile are generated through linear regression by using measurements. Similarly, in [8], the development of photovoltaic soiling maps is proposed. A map of dust intensity around the world is presented in [12].…”

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confidence: 99%

Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation

2020

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This paper addresses a methodology to optimize PV panel cleaning schedule. PV system yield is impacted due to soiling on panel surface. This soiling changes during the system operation as a consequence of environmental conditions. In order to reduce the impact of soiling accumulation, the panel surface must be cleaned. However, cost and frequency of cleaning can affect plant revenue. Therefore, to enhance financial performance, an optimal cleaning strategy must be selected. This strategy depends mostly on energy cost, soiling rate, cleaning costs, and system efficiency. Hence, the model proposed in this work takes into account these variables in order to support the decisions during the assessment of PV yield. The methodology was evaluated through two irradiation profiles with different associated costs. As a result, the methodology made it possible to estimate a cleaning strategy optimizing the incomes under different scenarios. INDEX TERMS Cleaning schedule optimization, fourier series, solar energy yield, soling ratio. I. INTRODUCTION Photovoltaic (PV) generation is considered as a solution to face the issues related to climate change. The use of PV is expected to reduce the emissions produced by electricity generation. Traditional power generation using coal, gas, water, among others, as primary sources is considered as mature and optimized technology. However, the integration of new renewable generation including PV posses challenges during their planning, operation, and maintenance [1]. Accurate estimation of return on investment is a concern of the current planning of PV projects to minimize financial risk. The assessment of return on investment depends on the prediction of power delivery which is influenced by the performance of PV-system. In [2], the factors that influence the system performance are classified by environment, PV system, installation, cost, and miscellaneous. The environmental factors include the solar irradiance, temperature, shades, dust and soiling. As for PV-system factors the I-V characteristics, inverter efficiency, battery efficiency, PV technology, The associate editor coordinating the review of this manuscript and approving it for publication was Dwarkadas Pralhaddas Kothari.

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Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI)

Cited by 23 publications

References 34 publications

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Design of a Low-Cost Multiplexer for the Study of the Impact of Soiling on PV Panel Performance

Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation

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