Remote Monitoring and Diagnosis System for Wind Turbines Using IoT

Abstract

This research focuses on developing an intelligent remote monitoring and diagnostic system for wind turbines leveraging the Internet of Things (IoT). The system aims to improve operational efficiency, reduce downtime, and optimize maintenance schedules by providing real-time data analysis and predictive diagnostics. Through the integration of various sensors, data acquisition, and machine learning algorithms, the system identifies potential faults and predicts failures before they occur. The results demonstrate a significant reduction in maintenance costs and improved turbine lifespan.

Introduction

Wind energy is a crucial renewable energy source, but maximizing its efficiency and minimizing downtime are critical. Traditional wind turbine maintenance relies heavily on scheduled inspections, which are costly and may not be optimal. The increasing complexity of wind turbines necessitates a more proactive approach. Existing monitoring systems often lack the sophistication to perform accurate predictive diagnostics. This research addresses this gap by proposing an IoT-based system that leverages real-time data analysis and machine learning to enable predictive maintenance and improved decision-making.

Objectives

• Develop a real-time data acquisition system for wind turbine parameters.
• Implement predictive maintenance algorithms for fault detection and prediction.
• Create a user-friendly interface for visualizing data and receiving alerts.

Technical Details

• IoT sensors for monitoring wind turbine parameters (vibration, temperature, rotation speed, etc.).
• Microcontroller (e.g., ESP32) for data collection and Wi-Fi communication.
• Cloud platform for data storage and machine learning-based diagnostics.
• Web dashboard for alert generation and performance visualization.
• Automated fault detection and predictive analytics module.