4.7 Article

Experimental investigation and modeling of photovoltaic soiling loss as a function of environmental variables: A case study of semi-arid climate

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ELSEVIER
DOI: 10.1016/j.solmat.2020.110874

关键词

PV soiling Prediction; Dust analysis; Performance ratio; Energy drop; Response surface methodology; Artificial neural networks

资金

  1. Research Institute of Solar Energy and New Energies (IRESEN)

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The study found that the energy drop per day reached 0.43 kWh/day during dry periods and 0.03 kWh/day during rainy periods, with an expected energy production of 5.59 kWh/day. The daily performance ratio dropped by an average of 6.1%/day and 1.6%/day during dry and rainy periods respectively. The soiling ratio during the dry period reached an average of 0.35%/day.
Photovoltaic technology is still developing in the MENA region. Nevertheless, soiling remains a major cause for performance loss in PV Modules. In this work, the soiling rate is modeled as a function of environmental data with several modeling methods such as the multiple linear regression model (MLR), multiple linear regression with interaction model (MLRWI), the mathematical model generated by the response surface methodology (RSM) and Artificial Neural Networks (ANNs), by using one-year of ground measurements from an amorphous array with a capacity of 2.16 kWc. The experiment is carried under a semi-arid climate at Green Energy Park research facility (Benguerir, Morocco). The dust analysis was carried out by Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and X-ray fluorescence (XRF) in two periods (December 2017 and June 2018) in order to define the mineralogy and morphology of our dust samples. The results of this study show that, the daily energy drop reaches 0.43 kWh/day and 0.61 kW/h/day in the dry period and 0.03 kW/h/day in the rainy period, where the expected produced energy is 5.59 KWh/day. The daily performance ratio drop reaches an average of 6.1%/day and 1.6%/day in the dry and rainy period respectively. During the dry period the soiling ratio reaches an average of 0.35%/day. The MLR method marked the lowest correlation with r(2) = 0.23, this correlation improved to reach r(2) = 0.48 by using the MLRWI method. ANN model shows the best performance and accuracy with r(2) = 0.813 and around 0.026 in the RMSE indicator.

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