A Trend-Based IoT Monitoring and Automated Cleaning Framework for Solar PV Systems Under Dust and Salinity Conditions

Dust, humidity, and salt deposition are some of the challenges faced by PV systems in coastal areas that can impact the output of the electrical system and make them more prone to maintenance. Typical maintenance methods are guided by manual cleaning or by monitoring a single parameter only and can lead to unnecessary cleaning and/or delayed maintenance. A smart PV monitoring and automated cleaning system for coastal area based on Internet of Things (IoT) is proposed in this paper. The proposed system includes voltage, current, dust and salinity sensors, an autonomous solar-panel cleaning robot and an ESP32 controller. The Trend Based Cleaning Algorithm compares the PV voltage it expects, the measured PV voltage, a performance loss per unit time, salinity level, and the time since the last cleaning operation. Cleaning is started only when the pre-set performance-loss and salinity requirements are met. A 7 day simulation and hardware experiments were performed. The salinity losses were from 2.1% to 5.0% and the performance losses were 4.2% to 9.8% measured in the hardware. The results of the simulation and hardware showed to be in good agreement the trends, differences were due to the environmental conditions, sensor tolerances and measurement noise. The automated cleaning cycle was about 8-12 minutes, which is less time than manual cleaning takes, which is reported as 30-40 minutes; the project reports a 70% reduction in maintenance labour. The findings show that such a comprehensive system (environmental sensing, electrical performance analysis, IoT monitoring and autonomous cleaning) for PV maintenance in a challenging coastal environment is feasible.