A Comprehensive Review of Solar Panel Cleaning Systems and Their Impact on Electric Conversion Efficiency

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

The global transition toward renewable energy has positioned photovoltaic (PV) systems as a cornerstone of sustainable power generation. However, the efficiency of solar PV modules is significantly compromised by soiling—the accumulation of dust, dirt, bird droppings, and other contaminants on panel surfaces. This review synthesizes findings from recent literature to examine the multifaceted impact of soiling on PV performance and evaluate the effectiveness of various cleaning methodologies. Soiling-induced power losses range from 5–35% globally, reaching up to 50–65% in arid and desert regions. The adverse effects extend beyond optical obstruction to include thermal stress, hotspot formation, and accelerated material degradation. In this paper, cleaning techniques are classified into conventional techniques such as manual, water-based, brush/wiper systems and advanced techniques such as robotic systems, superhydrophobic coatings, electrostatic screens, and ionic wind technologies. Special emphasis is given to emerging waterless and contactless technologies addressing water scarcity challenges in solar-rich regions. The review concludes that although superhydrophobic coatings provide passive self-cleaning with 90–96% transparency and water contact angles above 150°, long-term durability remains a challenge. Electrodynamic screens have dust removal efficiencies of 80–90% under dry conditions, but performance decreases under high humidity. Robotic cleaning systems may increase energy yield by 9–35% despite large capital expenditure requirements. The paper concludes with a call for integrated approaches combining multiple technologies and suggests future research directions involving self-healing coatings, predictive cleaning powered by artificial intelligence, and hybrid systems to optimize the trade-off between cleaning frequency, water consumption, and energy gain.

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