Adebayo A. Aremu, Ayotunde O. Fasina, OYEYEMI T. AFOROLAGBA-BALOGUN
The growing adoption of photovoltaic (PV) solar energy systems has increased the demand for efficient maintenance approaches that sustain power generation and improve long-term operational performance. One of the major factors reducing photovoltaic efficiency is the accumulation of dust, dirt, bird droppings, and other contaminants on panel surfaces, which obstruct solar irradiance absorption and decrease energy output. Conventional manual cleaning methods are labor-intensive, inconsistent, time-consuming, and may expose operators to safety risks, particularly in rooftop and large-scale installations. This study designed, developed, and evaluated an automated solar panel cleaning system integrated with a water spray mechanism to enhance photovoltaic performance and minimize maintenance requirements. The developed system employed a hybrid cleaning mechanism combining mechanical brushing and controlled water spraying under microcontroller-based automation. Major system components included an Arduino/ESP32 controller, dust and light sensors, a DC gear motor, soft-bristle cleaning brush, mini-DC water pump, spray nozzles, water reservoir, guide rails, and an independent power supply. A closed-loop control strategy was implemented to continuously monitor environmental conditions and contamination levels. When contamination exceeded a predefined threshold, the controller automatically activated a two-stage cleaning process involving water-assisted contaminant loosening followed by mechanical brushing for complete surface restoration. Engineering design calculations were incorporated to optimize flow rate, pump performance, spray distribution, and energy consumption. Experimental validation was conducted using a 50 W monocrystalline photovoltaic panel under real environmental conditions over a seven-day period. Key performance indicators including voltage, current, irradiance, dust accumulation, and cleaning effectiveness were measured before and after cleaning cycles. Results showed a significant improvement in photovoltaic performance, with average panel output increasing from 29 W under dusty conditions to 35 W after cleaning, representing an efficiency gain of approximately 20.4%. Peak improvement reached 22.8% during periods of maximum irradiance. The integrated water spray subsystem improved removal of adhered dust while reducing surface friction and cleaning wear. The study concludes that the automated cleaning system provides an effective, low-cost, and energy-efficient solution for maintaining photovoltaic performance and supports future integration with IoT-enabled predictive maintenance systems.