IoT-Based Dual-Axis Solar Panel Positioning and Fault Detection for Optimal Energy Output

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 311-316, March 2026.

https://doi.org/10.58482/ijersem.v2i3.40

This work is licensed under a Creative Commons Attribution 4.0 International License.

IoT-Based Dual-Axis Solar Panel Positioning and Fault Detection for Optimal Energy Output

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D S Vanaja

V Kaveri

K Bavana

P Ganesh

P Hari Siva

M Ajith Kumar

1Assistant Professor, Department of ECE, Siddartha Institute of Science and Technology, Puttur, India.

2-6UG Scholar, Department of ECE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract: This paper presents an Internet of Things (IoT)-based dual-axis solar tracking and fault detection system designed to optimize solar energy harvesting and ensure efficient monitoring of the system’s health. The system utilizes four Light Dependent Resistors (LDRs) to track the sun’s position on two axes, enabling real-time adjustments of the solar panel’s orientation using two motors, maximizing energy capture throughout the day. The energy harvested is managed through a charging circuit and stored in a battery, which powers the system. For real-time fault detection, a separate LDR and voltage sensor monitor the panel’s performance, detecting abnormalities such as shading or faults in energy output. This data is processed by an ESP32 microcontroller, which drives the motors via a motor driver, and displays system status on a 16×2 LCD screen. The data is also transmitted to an IoT platform (UBIDOTS) via an ESP8266 NodeMCU, enabling remote monitoring and alert notifications to be sent to the user’s email. This combination of solar tracking and fault detection improves the system’s reliability, efficiency, and responsiveness, making it a robust solution for remote solar installations.

Keywords: Fault Detection, Solar Panel Positioning, Solar Tracking, Optimum Energy, LDR.

References: 

  1. R. A. M. Rudro et al., “SPFNet2: A lightweight solar panel fault detection framework using parallel U-Net and MobileNetV3Large,” Renewable Energy, vol. 261, p. 125235, Jan. 2026, doi: 10.1016/j.renene.2026.125235.
  2. M. Yakut and N. B. Erturk, “An IoT-based approach for optimal relative positioning of solar panel arrays during backtracking,” Computer Standards & Interfaces, vol. 80, p. 103568, Aug. 2021, doi: 10.1016/j.csi.2021.103568.
  3. X. Yang, H. Fang, F. Yang, K. Li, R. Han, and T. Li, “A lightweight detector with hybrid pooling and checkerboard attention for solar panel anomalies,” iScience, vol. 29, no. 3, p. 115106, Feb. 2026, doi: 10.1016/j.isci.2026.115106.
  4. A. M. A. S. Azad et al., “Harnessing the sun: Framework for development and performance evaluation of AI-driven solar tracker for optimal energy harvesting,” Energy Conversion and Management X, vol. 26, p. 100990, Mar. 2025, doi: 10.1016/j.ecmx.2025.100990.
  5. R. D. A. Raj, R. M. R. Yanamala, A. Pallakonda, J. Aghaei, and E. Pouresmaeil, “Integrative deep learning architectures and convolutional neural networks for advanced fault classification in photovoltaic modules,” Sustainable Energy Technologies and Assessments, vol. 85, p. 104725, Dec. 2025, doi: 10.1016/j.seta.2025.104725.
  6. H. Mohapatra, “A comprehensive review on urban resilience via Fault-Tolerant IoT and sensor networks,” Computers, Materials & Continua/Computers, Materials & Continua (Print), vol. 85, no. 1, pp. 221–247, Jan. 2025, doi: 10.32604/cmc.2025.068338.
  7. K. Shahrouri, A. G. Tolba, M. H. Doranehgard, and A. Vaselbehagh, “Optical losses in photovoltaic solar panels: Mechanisms, modeling approaches, and mitigation strategies,” Thermal Science and Engineering Progress, vol. 71, p. 104557, Feb. 2026, doi: 10.1016/j.tsep.2026.104557.
  8. N. Kuttybay et al., “Assessment of solar tracking systems: A comprehensive review,” Sustainable Energy Technologies and Assessments, vol. 68, p. 103879, Jun. 2024, doi: 10.1016/j.seta.2024.103879.
  9. U. Mamodiya, I. Kishor, P. Vidyullatha, M. Almaayah, and A. Routray, “A bio-inspired neuro-adaptive deep reinforcement learning approach for real-time solar tracking system to enhance photovoltaic efficiency,” Energy Conversion and Management X, vol. 29, p. 101486, Dec. 2025, doi: 10.1016/j.ecmx.2025.101486.
  10. D. Joshi and M. Pal, “Lightweight transformer driven segmentation of defects in solar PV thermal imagery,” Solar Energy, vol. 308, p. 114402, Feb. 2026, doi: 10.1016/j.solener.2026.114402.
  11. W. Dong, J. Peng, X. Guo, Y. Li, Z. Liu, and H. Sun, “Fault detection and diagnosis of hybrid hydrogen production systems with dynamic deep coupled dictionary learning,” International Journal of Hydrogen Energy, vol. 206, p. 153459, Jan. 2026, doi: 10.1016/j.ijhydene.2026.153459.
  12. P. S. Paliyal and S. Mondal, “Parabolic solar collectors for sustainable water treatment: A review of applications, advancements and future directions,” Current Opinion in Environmental Science & Health, vol. 49, p. 100691, Nov. 2025, doi: 10.1016/j.coesh.2025.100691.
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This article is part of Volume 2, Issue 3 (March 2026)