An Artificial Intelligence-based Fault Detection and Diagnosis System for Wind Turbines

Abstract

Wind turbines are complex mechanical systems that require continuous monitoring and maintenance to ensure optimal performance and reliability. The early detection and diagnosis of faults in wind turbines can prevent costly repairs and minimize downtime, leading to increased efficiency and profitability. In this paper, we present an artificial intelligence-based fault detection and diagnosis system for wind turbines that can accurately identify and diagnose faults in real-time.
The proposed system uses machine learning algorithms to analyze data from various sensors installed in the wind turbine, including vibration sensors, temperature sensors, and current sensors. The system is trained on a dataset of historical data that includes normal and fault conditions. The machine learning algorithms are then used to detect anomalies in the data and identify the type and severity of the fault.
To evaluate the performance of the system, we conducted a case study using data collected from a wind farm located in a coastal region. The system was able to detect faults in real-time and accurately diagnose the type and severity of the fault. The system was also able to provide recommendations for maintenance and repair, leading to reduced downtime and increased efficiency.
In addition, we performed a comparative analysis of the proposed system with traditional fault detection and diagnosis methods, such as rule-based systems and expert systems. The analysis shows that the proposed system outperforms traditional methods in terms of accuracy, speed, and scalability.
Overall, the proposed artificial intelligence-based fault detection and diagnosis system for wind turbines has the potential to revolutionize the maintenance and repair of wind turbines. The system can provide real-time monitoring and diagnosis, leading to reduced downtime, increased efficiency, and improved profitability. Further research and development are needed to optimize the system and increase its applicability to different types of wind turbines and operating conditions.