AI-Enhanced IoT-Based Solar Panel Fault Detection Device Using Edge AI, Machine Learning, and Digital Twin Technology for Real-Time Photovoltaic System Monitoring
Jul 2026· International Journal Of Recent Trends In Multidisciplinary Research· pp. 72· 0 citations
TL;DR
The proposed AI-based IoT solution offers a low-cost, scalable, energy-efficient, and reliable platform for intelligent photovoltaic system monitoring, predictive maintenance, and smart renewable energy applications, making it suitable for residential, commercial, and remote solar installations.
Abstract
Solar photovoltaic (PV) systems require continuous monitoring to maintain energy efficiency, minimize power losses, and detect operational faults at an early stage. This paper presents an AI-enhanced Internet of Things (IoT)-based solar panel fault detection device that performs real-time monitoring, intelligent fault diagnosis, and edge-based data processing. The proposed system integrates multiple environmental and electrical sensors with an ESP32 microcontroller to collect voltage, current, temperature, irradiance, and other operating parameters. A lightweight Decision Tree machine learning model, optimized using TensorFlow Lite, is deployed on the edge device to classify common solar panel faults, including dust accumulation, partial shading, temperature imbalance, electrical anomalies, and panel degradation. Edge AI processing significantly reduces detection latency, minimizes cloud dependency, and enables rapid fault identification in remote environments. The developed prototype was validated using both synthetic datasets and real-time sensor measurements obtained from an operational solar panel setup. Experimental evaluation demonstrated a fault detection accuracy ranging from 92% to 100% with an average response time of less than one second. A digital twin-enabled web dashboard was also developed to provide real-time visualization, remote monitoring, historical data analysis, and fault alerts for enhanced system management. The proposed AI-based IoT solution offers a low-cost, scalable, energy-efficient, and reliable platform for intelligent photovoltaic system monitoring, predictive maintenance, and smart renewable energy applications, making it suitable for residential, commercial, and remote solar installations.
The increasing demand for renewable energy has highlighted the need for efficient and reliable management of solar power plants. Solar plants, whether residential or industrial, require continuous monitoring to ensure optimal performance, detect faults, and maximize energy generation. Manual supervision is often time-consuming, prone to errors, and insufficient for detecting real-time anomalies. To address these challenges, this paper presents an IoT-based Solar Plant Monitoring System that enables remote, real-time observation and management of solar energy systems. The proposed system integrates sensors, microcontrollers, and IoT-enabled communication modules to collect key parameters such as solar panel voltage, current, temperature, and battery status. These parameters are transmitted over the internet to a centralized cloud platform, allowing plant operators to monitor system performance from any location using smartphones, laptops, or tablets. Alerts are generated automatically in case of abnormal readings, such as low voltage, panel overheating, or battery faults, facilitating proactive maintenance and minimizing downtime. By leveraging IoT technology, the system not only improves the efficiency and reliability of solar power generation but also reduces operational and maintenance costs. The proposed system supports data logging, historical analysis, and performance optimization. Additionally, the integration of cloud-based monitoring ensures scalability and flexibility, allowing expansion to multiple solar plants and large-scale installations.
S. S., A. S, D. K. et al.· International Conference Com...· 0 citations
The rapid expansion of offshore wind farms has introduced significant challenges to operation and maintenance (O&M), particularly under harsh marine environments where reliable electromagnetic information transmission and constrained wireless communication resources directly affect intelligent monitoring performance. Traditional Supervisory Control and Data Acquisition (SCADA) systems relying on cloud-centric architectures often encounter excessive latency and bandwidth bottlenecks when transmitting high-frequency vibration signals, limiting real-time fault diagnosis. To address these issues, this study proposes an Internet of Things (IoT)-based intelligent monitoring and fault detection method built upon an Edge-Cloud collaborative architecture. A lightweight Adaptive One-Dimensional Convolutional Neural Network (A-1D-CNN) is developed for deployment on edge gateway devices, enabling direct extraction of fault characteristics from raw vibration signals without manual feature engineering. Combined with an “ Edge-Training, Cloud-Update” strategy, the proposed framework continuously optimizes diagnostic performance while substantially reducing communication overhead across wireless sensing and electromagnetic transmission infrastructures. Experimental evaluation on a standard bearing fault dataset demonstrates that the proposed method achieves a fault diagnosis accuracy of 99.25% with a compact model size of only 0.45 MB, providing an effective balance between diagnostic precision and deployment efficiency. The results indicate that the proposed framework offers a practical solution for real-time intelligent monitoring in bandwidth-limited offshore environments and provides technical support for reliable electromagnetic-enabled sensing networks and distributed fault diagnosis in next-generation offshore energy systems.
Y. Ouyang, W. Liang· Advanced Electromagnetics· 0 citations
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety hazards. To address these challenges, this paper presents the design and implementation of an Internet of Things (IoT)-enabled real-time machine health monitoring and protection system based on the ESP32 microcontroller platform. The proposed system integrates a DHT11 sensor for temperature and humidity monitoring, an ACS712 Hall-effect sensor for current measurement, and an MPU9250 inertial measurement unit (IMU) for vibration analysis. Sensor data are continuously acquired, processed, and transmitted through Wi-Fi to a cloud-based Firebase Realtime Database, enabling remote access and centralized monitoring. A responsive web dashboard hosted on GitHub Pages provides real-time visualization of machine operating parameters, status indicators, and fault notifications. To enhance operational safety and equipment reliability, threshold-based fault detection algorithms are implemented to identify abnormal operating conditions. When predefined critical limits are exceeded, the ESP32 automatically initiates protective actions by disconnecting the machine through a relay module, activating a visual alarm, and updating the fault status on the cloud platform. The dashboard additionally supports bidirectional communication, allowing authorized operators to remotely restart the machine, while a local push-button interface enables manual system recovery. Furthermore, the developed platform incorporates a browser-based logging mechanism that records timestamped sensor measurements, machine status transitions, fault events, and downloadable CSV trend data for maintenance analysis and performance evaluation. Experimental validation demonstrates reliable real-time monitoring with a data refresh interval of approximately 3 s, accurate threshold-based fault detection, dependable cloud connectivity, and effective remote supervisory control. The proposed solution offers a low-cost, scalable, and practical framework for predictive maintenance and industrial equipment condition monitoring in smart manufacturing environments.
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
The development of intelligent solar energy management systems has increased due to the growing demand for renewable energy and the requirement for improved photovoltaic (PV) efficiency. Dust buildup, shifting sunlight angles, and ineffective monitoring systems cause conventional solar panels in fixed positions to extract minimum energy. This work suggests an IoT, Raspberry Pi and AI-based smart solar tracking with monitoring system, as a solution to these problems. To optimize solar power production, the suggested system combines dual-axis sun tracking, wireless monitor-ing, real-time sensing, and automated cleaning. Sensors are used to continually monitor electrical and environmental factors such panel voltage, current, power, temperature, and light intensity. The main controller for data collection, processing, and actuator control is a Raspberry Pi 3B+. In order to maximize sunshine exposure, the system dynamically modifies panel orientation using twin DC motors based on inputs from LDR and BH1750 sensors. Remote monitoring via a web dashboard is proposed by IoT connectivity. According to experimental findings, tracking mode produced an average output voltage of 12.24 V as opposed to 11.35 V in stationary mode, a 7.82% improvement is observed. Additionally, the suggested approach minimized dust-related efficiency losses through automated cleaning and average power generation increased by about 18.6%. Smart maintenance recommendations, anomaly detection, and performance prediction are further presented by AI-based analytics. The findings show that Intelli Solar offers next-generation smart photovoltaic energy systems an effective, affordable, and scalable solution.
Sushree Samikshya Pattanaik, Rajesh Panda, Vandana Jha et al.· International journal of com...· 0 citations
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments plays a vital role in minimizing unplanned downtime, improving operational efficiency, and spreading equipment lifespan. This paper presents a Machine Learning (ML)-based predictive maintenance basis deployed on Google Cloud AI Platform for real-time monitoring and fault prediction of manufacturing milling machine devices. The proposed system develops sensor-generated operational data, including torque, rotational speed, temperature, and tool wear, to train and evaluate multiple ML models such as Decision Tree, K-Nearest Neighbors (KNN), Gradient Boosting, Support Vector Machine (SVM), Gaussian Naïve Bayes, and Logistic Regression. The confirmed models, the Decision Tree classifier reached the highest accuracy of 99.40%, with strong cross-validation and AUC performance, indicating larger capability in detection machine failures. By fit in cloud-based AI services, the framework ensures scalable model deployment, high availability, and efficient real-time predictive analytics for manufacturing applications. Experimental findings reveal important improvements in prediction accuracy and conservation cost reduction associated to conventional reactive maintenance approaches. The study confirms the efficiency of combining IIoT sensor analytics, ML, and cloud-based AI structure for intelligent and proactive industrial conservation systems.
More Praveen, A. Lakshman, V.Jyothi et al.· International Conference Com...· 0 citations
