Aegus: From Manual Risk to Autonomous Safety

Transforming solar panel maintenance with IoT-driven automation for enhanced safety, precision, and operational efficiency.

Client: Aegus Pvt Ltd
Industry: Renewable Energy / Solar Infrastructure Automation

Challenge

High-Risk Manual Operations and Limited Scalability

Manual cleaning of utility-scale solar panels posed significant operational challenges — ranging from severe safety risks at elevated heights to inconsistent cleaning efficiency and high operational expenditures (OPEX) caused by heavy human supervision.

  • Safety Hazards: Manual work at height increased fall risks, equipment handling hazards, and severe heat exposure.
  • Inconsistent Cleaning: Manual control resulted in uneven dust removal, leading to suppressed solar power yields.
  • High Labor Cost: Required multiple on-site technicians and continuous supervision.
  • Poor Scalability: Heavy human dependency restricted rapid expansion across large solar farms.
Solution

IoT-Based Autonomous Solar Cleaning System

SunBio IT Solutions engineered a fully autonomous, IoT-enabled solar panel cleaning system that minimized on-site risk, maximized photovoltaic performance, and allowed remote supervision through intelligent sensor integration and cloud connectivity.

  • Intelligent Sensor Suite: Equipped with LiDAR and ultrasonic sensors for precise obstacle avoidance and safe navigation across panel arrays.
  • Real-Time Telemetry: An IoT-enabled cloud dashboard providing live operational monitoring and performance analytics.
  • Remote Operation: Centralized scheduling and equipment control manageable via desktop or mobile interfaces.
  • Smart Power Management: Optimized, autonomous battery utilization designed to sustain complete cleaning cycles.
Results

Zero Safety Incidents and 50% Faster Cleaning Cycles

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Metric Before Automation After Automation Improvement
Safety Incidents (at height) Moderate Risk Zero Risk 100% Risk Elimination
Cleaning Cycle Time 8 hrs (Manual) 4 hrs (Autonomous) 50% Faster
Labor Cost 2 Supervisors/site 0.1 Remote Operator 95% Reduction
Energy Loss (Soiling) 4–6% <1% +5–7% Uptime

By automating the entire workflow, Aegus successfully eliminated physical safety incidents, protected equipment integrity, maximized clean energy generation, and drastically reduced day-to-day operating expenses.

Next Phase

AI-Driven Predictive Maintenance & SCADA Integration

  • Predictive Scheduling: Deployment of machine learning models to determine optimal cleaning schedules based on real-time local soiling patterns and weather forecasts.
  • SCADA Integration: Seamless linkage of real-time cleaning metrics with broader solar farm SCADA and performance monitoring systems.
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