DESIGN AND DEVELOPMENT OF A LOW-COST, IOT-ENABLED AUTONOMOUS SYSTEM FOR SOLAR PHOTOVOLTAIC PANEL CLEANING

Abstract

Soiling caused by dust, pollen, and bird droppings is one of the most persistent factors limiting the energy yield of photovoltaic (PV) installations, with reported losses ranging from 3–8% under normal conditions to as high as 50% in arid, dust-prone regions. Manual cleaning remains labor-intensive, water-intensive, and often unsafe for rooftop or large-scale arrays, while existing robotic solutions are predominantly rail-mounted, expensive, and structurally invasive, making them impractical for small-to-medium-scale or residential installations. This paper presents the design, implementation, and evaluation of an Autonomous Solar Panel Cleaning System (ASPCS-R1), a lightweight, rail-free, mobile cleaning robot built around an ESP32 microcontroller. The system integrates a high-torque rotating brush, a low-volume water-misting mechanism, differential-drive wheel motors, and limit-switch-based edge detection to autonomously traverse and clean a panel surface without human supervision, while Wi-Fi-based IoT connectivity (via the Blynk platform) enables remote monitoring and manual override. A kinematic analysis of the drive system establishes a maximum linear traversal speed of 0.712 m/s and an operating speed of 0.348 m/s at a 48.85% pulse-width-modulation (PWM) duty cycle. Compared with pressure-washing-based manual cleaning, the proposed system is estimated to reduce water consumption by approximately 92%, using roughly 0.5 L/m² per cleaning cycle, while eliminating the structural and cost barriers associated with rail-mounted commercial systems. The results indicate that the proposed design offers a scalable, water-efficient, and economically viable alternative for maintaining PV efficiency in resource-constrained and decentralized installations.