Photovoltaic cleaning optimization through the analysis of historical time series of environmental parameters
Leonardo Micheli, Eduardo F. Férnández, Florencia Almonacid
Abstract
This work investigates the possibility of using historical environmental parameter data to predict the typical soiling loss profile and the most convenient cleaning schedule for a PV site. The three-year performance of a 1 MW system in Southern Spain is evaluated using different soiling extraction methods. When the rainfall pattern is used to detect natural cleaning events, the best results are obtained if a 1.0 mm/hour threshold is considered. However, despite the optimization, setting a fixed threshold is found to lead occasionally to the over- or under-detection of cleaning events. Similar trends in the modelling results are found if the thresholds are set using the maximum hourly or the cumulative daily rainfall data, but the errors and the optimal values change depending on the rainfall dataset. The study also shows that a soiling extraction method based only on precipitation and particulate matter, calibrated against one year of PV data, is able to generate a soiling profile with a mean absolute error of 0.022 and to recommend a cleaning day within a week of the actual optimal dates. This will make it possible to estimate the soiling losses and the optimal cleaning schedule for a PV site even if no power data are available.