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

Leonardo Micheli, Marios Theristis, Andreas Livera, Joshua Stein, George Georghiou, Matthew Muller, Florencia Almonacid, Eduardo Fernandez

Research output: Contribution to journalArticlepeer-review

27 Scopus Citations

Abstract

Photovoltaic (PV) soiling profiles exhibit a sawtooth shape, where cleaning events and soiling deposition periods alternate. Generally, the rate at which soiling accumulates is assumed to be constant within each deposition period. In reality, changes in rates can occur because of sudden variations in climatic conditions, e.g., dust storms or prolonged periods of rain. The existing models used to extract the soiling profile from the PV performance data might fail to capture the change points and occasionally estimate incorrect soiling profiles. This work analyzes how the introduction of change points can be beneficial for soiling extraction. Data from nine soiling stations and a 1-MW site were analyzed by using piecewise regression and three change point detection algorithms. The results showed that accounting for change points can provide significant benefits to the modeling of soiling even if not all the change point algorithms return the same improvements. Considering change points in historical trends is found to be particularly important for studies aiming to optimize cleaning schedules.

Original languageAmerican English
Article number9312967
Pages (from-to)519-526
Number of pages8
JournalIEEE Journal of Photovoltaics
Volume11
Issue number2
DOIs
StatePublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2011-2012 IEEE.

NREL Publication Number

  • NREL/JA-5K00-78766

Keywords

  • Monitoring
  • photovoltaic (PV) systems
  • regression analysis
  • soiling
  • time-series analysis

Fingerprint

Dive into the research topics of 'Improved PV Soiling Extraction Through the Detection of Cleanings and Change Points'. Together they form a unique fingerprint.

Cite this