An AI and IoT-Enabled LoRaWAN Framework for Real-Time Wind Turbine Condition Monitoring and Predictive Analytics

S Kumarappa

LoRaWAN, Internet of Things (IoT), Artificial Intelligence (AI), wind turbine monitoring, predictive maintenance, LPWAN, ESP32, renewable energy systems.

This study introduces an intelligent monitoring framework that combines Artificial Intelligence (AI) and Internet of Things (IoT) technology using the LoRaWAN communication protocol for real-time condition monitoring of wind turbines. The framework improves operational dependability, lowers maintenance costs, and increases power generation efficiency by enabling remote, intelligent, and predictive oversight of turbine components. Operating at 433 MHz, it employs LPWAN technology to support long-range, low-power communication between turbine nodes and a central monitoring station. A suite of sensors—tracking voltage, current, temperature, vibration, and wind speed—is connected to ESP32 microcontrollers to collect electrical, mechanical, and environmental data from the WTG. The ESP32 digitizes sensor signals and transmits them via LoRa to a receiver node with an ESP32–LoRa module, which functions as a gateway and forwards the data to a cloud server over Wi-Fi. The cloud platform uses AI algorithms to automatically analyze the data, detect anomalies, and forecast failures through machine learning models trained on historical datasets. Testing demonstrated a packet delivery rate above 94% across a 3 km range with very low latency, and the AI module successfully identified irregularities and predicted potential faults. Thanks to its modular design, the system can be easily customized for different renewable energy applications. In conclusion, it offers a scalable, dependable, and sustainable solution for intelligent monitoring and predictive maintenance in modern wind turbine installations.

Published Paper   E-Certificate

"An AI and IoT-Enabled LoRaWAN Framework for Real-Time Wind Turbine Condition Monitoring and Predictive Analytics", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a588-a599, January-2026, Available :https://ijsdr.org/papers/IJSDR2601063.pdf

Volume 11 Issue 1, January-2026

Pages : a588-a599

Paper Reg. ID: IJSDR_306149

Published Paper Id: IJSDR2601063

Downloads: 000237

Research Area: Science and Technology

Country: Davangere, Karnataka, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601063

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601063

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition