Intelligent cleanup scheme for soiled photovoltaic modules

Humble Po-Ching Hwang, Cooper Cheng Yuan Ku*, Mason Chao-Yang Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In recent years, solar energy systems have increased significantly worldwide. However, over time, the efficiency of photovoltaic (PV) systems is always affected primarily by soiling deposits on the surfaces of PV modules. The soiling deposits lower the intensity of the irradiation transmittance, and the performance of the PV system is also reduced. Therefore, cleaning PV modules is a very routine and critical task. To reduce the efficiency loss caused by soiling deposits and increase lifetime revenue as much as possible, we propose an intelligent method for monitoring soiling status with a statistical approach, an image processing (IP) scheme, and a machine learning (ML) algorithm. Based on the experimental result, the accuracy of our method is 98.39% which indicates that it classifies the soiling status of solar panels excellently. Therefore, we believe the proposed method can assist maintenance personnel in determining the near-optimal policy of cleaning schedules for PV systems. This also decreases power loss and saves labor and time for long-term maintenance.

Original languageEnglish
Article number126293
JournalEnergy
Volume265
DOIs
StatePublished - 15 Feb 2023

Keywords

  • Image processing
  • Machine learning
  • Photovoltaic cleaning policy
  • Soiling detection
  • Statistics

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