Enhanced Photovoltaic Efficiency Using Dual-Axis Solar Tracking With Integrated Cleaning Alert System And Monitoring

Abstract

Conventional fixed photovoltaic (PV) panels lose significant output because the sun\'s position changes continuously and dust accumulates on the panel surface. This paper presents an enhanced PV system combining dual-axis solar tracking with a cleaning alert system and IoT real-time monitoring. Four LDR sensors and an ESP32 drive two DC motors through an L298N H-bridge to align the panel along the azimuth and elevation axes. A dual-channel alert, using INA219 power sensors and an ESP32-CAM, detects dust-related loss. Results show a ?42% improvement in daily energy yield over a fixed installation, ±2° tracking accuracy, and a dust-alert response under five minutes.

Introduction

The text discusses the importance of improving solar photovoltaic (PV) system efficiency as renewable energy adoption increases. Although solar energy is abundant and cost-effective, fixed solar panels lose energy due to changing sun angles and dust accumulation, which can reduce output by around 20–30%. Solar tracking systems help reduce orientation losses, with single-axis trackers improving yield by about 20–25% and dual-axis trackers by 30–40%.

The study proposes a low-cost integrated solar PV optimization system combining:

• Dual-axis solar tracking
• Dust detection and cleaning alerts
• IoT-based monitoring

The system uses an ESP32 microcontroller as the main controller. Four LDR sensors detect uneven sunlight intensity and control two DC motors to adjust the panel position in azimuth and elevation, keeping the panel aligned with the sun. Unlike fixed or time-based tracking methods, the design uses closed-loop feedback without requiring GPS or astronomical calculations.

A dual-channel dust detection system is introduced:

1. Electrical monitoring using INA219 sensors to detect drops in voltage/current/power.
2. Visual monitoring using an ESP32-CAM to compare panel brightness with a clean reference condition.

This combination reduces false dust alerts caused by temporary shading or clouds. The system also provides an IoT dashboard that displays real-time voltage, current, power, energy generation, dust status, and historical data through Wi-Fi communication.

The literature review highlights that:

• Dual-axis tracking can significantly improve PV output by maintaining optimal sunlight incidence.
• LDR-based feedback tracking is inexpensive and effective for small-scale solar systems.
• Dust accumulation is a major efficiency issue and requires automated monitoring solutions.
• IoT monitoring improves maintenance and performance analysis.

The proposed architecture consists of four main units:

• Sensing unit: LDR sensors and INA219 power sensors collect environmental and electrical data.
• Control unit: ESP32 processes sensor readings, manages tracking logic, and handles communication.
• Actuation unit: DC gear motors with an L298N driver move the solar panel.
• Monitoring/alert unit: Provides real-time data visualization and dust warnings.

Conclusion

This paper presented an enhanced PV system integrating dual-axis solar tracking, a dual-channel cleaning alert, and IoT-based monitoring on a single ESP32 platform, addressing three coupled limitations of fixed installations: inefficient orientation, undetected dust, and lack of performance feedback. The LDR-based closed-loop tracking held alignment within ±2°, and experiments showed an approximately 42% improvement in daily energy yield. The dual-mode alert responded to simulated soiling within five minutes, while the IoT dashboard gave continuous, low-latency visibility into voltage, current, power, battery state, and historical energy. The system\'s low cost, compact design, and wireless connectivity suit small-scale residential, institutional, and rural deployments, notifying users only when intervention is actually required. At larger scale, similar architectures could integrate with smart grids or distributed solar farms for IoT-enabled energy management. Future work will explore MPPT integration, ML-based irradiance forecasting, cloud-based predictive maintenance, and automated mechanical cleaning triggered by the existing alert, closing the loop between detection and remediation.

References

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Copyright

Copyright © 2026 Mrs. Ashidha K, Abdul Rahiman P. C., Ali Ashir K., Mohammed Suhail, Shibin Krishna K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.