Endogenous Soiling Rate Determination and Detection of Cleaning Events in Utility-Scale PV Plants

Abstract

As the deployment rate of PV power plants continues to soar, the need for robust, scalable methods for performance analytics increases. In this paper, we demonstrate the usefulness of one approach for quantifying soiling rates in utility-scale PV power plants endogenously, i.e., directly from the production data. The temperature corrected performance ratio, normalized to a clean state, is used to derive the soiling ratio (SR). Cleaning events, caused by either rain or manual cleaning, are automatically detected by positive shifts in the running median of the SR time series. Soiling rates are then estimated by the rate of change of the SR between the cleaning events, which is determined by linear regression. The method is validated on data from three utility-scale PV power plants in the Middle East, yielding soiling rates that are in the range 0%-0.18%/day at least 50% of the time, with a median of 0.1%/day.

Cleaning of Floating Photovoltaic Systems: A Critical Review on Approaches from Technical and Economic Perspectives

“…The dust accumulation effect on PV panels in the MENA region has been assessed in [118], and it has been noted that the cleaning interval can be 12-15 days. Using an endogenous method, the soiling rate in three utility-scale PV systems located in the Middle East has been calculated to be about 0.1% per day [125]. In [38], the optimal cleaning frequency of PV panels in a hot desert climate has been recommended to be weekly, especially during summers.…”

Monitoring photovoltaic soiling: assessment, challenges, and perspectives of current and potential strategies

“…The use of monthly mean soiling rates rather than a fixed soiling rate was proposed to represent better the soiling seasonality in FRP (Micheli et al, 2020a). Skomedal et al (Skomedal et al, 2019) proposed to modify the SRR cleaning detection equation in conditions of high data noise to d (Q 3 + IQR), with d being a site-specific parameter. In addition, they introduced some additional filters to remove outliers.…”

“…Skomedal et al. ( Skomedal et al., 2019 ) proposed to modify the SRR cleaning detection equation in conditions of high data noise to (Q 3 + IQR), with being a site-specific parameter. In addition, they introduced some additional filters to remove outliers.…”

Improved PV Soiling Extraction through the Detection of Cleanings and Change Points

“…Cleanings were identified in those dates in which a positive shift in the performance data larger than 3 + 1.5 • occurred. While some authors have suggested site-specific tuning of this equation [13], in this work it has been used in its original version; same for all the sites.…”