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Title: Quantifying Soiling Loss Directly From PV Yield

Abstract

Soiling of photovoltaic (PV) panels is typically quantified through the use of specialized sensors. Here, we describe and validate a method for estimating soiling loss experienced by PV systems directly from system yield without the need for precipitation data. The method, termed the stochastic rate and recovery (SRR) method, automatically detects soiling intervals in a dataset, then stochastically generates a sample of possible soiling profiles based on the observed characteristics of each interval. In this paper, we describe the method, validate it against soiling station measurements, and compare it with other PV-yield-based soiling estimation methods. The broader application of the SRR method will enable the fleet scale assessment of soiling loss to facilitate mitigation planning and risk assessment.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1419409
Report Number(s):
NREL/JA-5J00-70668
Journal ID: ISSN 2156-3381
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 2156-3381
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; field performance; Monte Carlo methods; photovoltaic cells; photovoltaic systems; soiling; solar energy; solar panels; time series analysis

Citation Formats

Deceglie, Michael G., Micheli, Leonardo, and Muller, Matthew. Quantifying Soiling Loss Directly From PV Yield. United States: N. p., 2018. Web. doi:10.1109/JPHOTOV.2017.2784682.
Deceglie, Michael G., Micheli, Leonardo, & Muller, Matthew. Quantifying Soiling Loss Directly From PV Yield. United States. doi:10.1109/JPHOTOV.2017.2784682.
Deceglie, Michael G., Micheli, Leonardo, and Muller, Matthew. Tue . "Quantifying Soiling Loss Directly From PV Yield". United States. doi:10.1109/JPHOTOV.2017.2784682. https://www.osti.gov/servlets/purl/1419409.
@article{osti_1419409,
title = {Quantifying Soiling Loss Directly From PV Yield},
author = {Deceglie, Michael G. and Micheli, Leonardo and Muller, Matthew},
abstractNote = {Soiling of photovoltaic (PV) panels is typically quantified through the use of specialized sensors. Here, we describe and validate a method for estimating soiling loss experienced by PV systems directly from system yield without the need for precipitation data. The method, termed the stochastic rate and recovery (SRR) method, automatically detects soiling intervals in a dataset, then stochastically generates a sample of possible soiling profiles based on the observed characteristics of each interval. In this paper, we describe the method, validate it against soiling station measurements, and compare it with other PV-yield-based soiling estimation methods. The broader application of the SRR method will enable the fleet scale assessment of soiling loss to facilitate mitigation planning and risk assessment.},
doi = {10.1109/JPHOTOV.2017.2784682},
journal = {IEEE Journal of Photovoltaics},
issn = {2156-3381},
number = 2,
volume = 8,
place = {United States},
year = {2018},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 5 works
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Figures / Tables:

Fig. 1 Fig. 1: Steps of the yield-based SRR soiling estimation method. Blue circles are the daily performance metric, PM. (a) Time series of PM is divided into soiling intervals by detecting positive shifts in the moving median (black line), which correspond to cleaning events (dashed lines). (b) Slope of each intervalmore » is extracted via the Theil–Sen method. The gray lines illustrate the lines between all possible pairs of points, the black line shows the best fit found from the median slope. (c) Possible soiling profiles are stochastically generated. The orange lines show a subset of these profiles. (d) Sample of rs,w values calculated from the stochastically generated profiles. The solid line shows the median, and the dashed lines indicate the 95% confidence interval.« less

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.