Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast...

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Bibliographic Details
Published in:IEEE journal of photovoltaics 2020-01, Vol.10 (1), p.197-205
Main Authors: Micheli, Leonardo, Fernandez, Eduardo F., Muller, Matthew, Almonacid, Florencia
Format: Article
Language:English
Subjects:
Algorithms
Cleaning
Data mining
Field performance
Maintenance management
Optimization
OTHER INSTRUMENTATION
photovoltaic (PV) systems
Photovoltaic cells
Photovoltaic systems
prediction methods
Rain
Rainfall
Schedules
Seasonal variations
Soil measurements
soiling
SOLAR ENERGY
stochastic processes
time series analysis
Weather
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Summary:The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.
ISSN:2156-3381
2156-3403
DOI:10.1109/JPHOTOV.2019.2943706