A holistic framework for PV performance optimization: Integrating intelligent solar tracking and autonomous robotic cleaning

Abstract

Maximizing the energy yield of photovoltaic (PV) systems requires a multi-faceted approach that addresses both optimal energy harvesting and proactive maintenance. This paper presents a holistic, intelligent framework that integrates two critical optimization strategies: dynamic dual-axis solar tracking to maximize incident irradiance and autonomous, rule-based robotic cleaning to mitigate soiling losses. The solar tracker, guided by a four-quadrant LDR sensor array, continuously adjusts the panel’s orientation to maintain perpendicularity with the sun’s rays, significantly boosting energy capture. Complementing this, the autonomous cleaning system leverages a differential data comparison between a soiled test panel and a clean reference panel to make informed decisions. Its rule-based engine triggers cleaning cycles only when performance degradation surpasses a defined threshold, avoiding unnecessary operations. A detailed design of the cleaning robot is presented, featuring a robust tracked locomotion system and a high-torque, dual-brush cleaning head. Experimental results demonstrate that the solar tracker increases daily energy generation by 30.1% on clear days and 115.4% on cloudy days. The cleaning algorithm effectively responds to soiling events while intelligently avoiding redundant cycles during natural cleaning events like rainfall. This integrated platform represents a comprehensive, practical solution for maximizing the lifecycle performance of PV installations.