Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

Abstract

Offshore wind turbines are prone to unexpected faults as they are often operated in a harsher environments compared to turbines on land. Accordingly, there has been a growing interest toward developing technologies for detecting and diagnosing faults in advance for the effective maintenance and operation of these turbines. Unexpected faults influence system components, actuators, sensors, and controllers, which may result in the inoperability of a turbine in case of serious failures. Moreover, faults can lead to economic losses by inducing changes in the system characteristics, operation safety, and power efficiency of a wind turbine. Maintenance costs account for approximately 25%-30% of the life-cycle cost of an offshore wind power plant (Dinwoodie et al., 2013), and therefore, the reliability of such a wind turbine is critical. Among the components of an offshore turbine, the blade pitch system has the highest downtime and failure rate (Gayo, 2011;Carroll et al., 2016;NordzeeWind, 2010). A system fault affects the aerodynamic loads of a blade, the power generation output, and the behavior of substructures. Therefore, it is crucial to diagnose faults at the early stage in a blade pitch system to protect a turbine and prevent downtime of the entire system (Cho et al., 2018). Early fault diagnosis can reduce risks and prevent accidents by raising alarms at appropriate times, thus enabling wind power plant operators to effectively operate and maintain a turbine (Isermann, 2006). It can also prevent long-term damage to turbines and provide a reliable technological guarantee for the further development of the wind power generation industry. Up to recently, classical fault diagnosis methods with statistical classification, approximation methods, and density-based methods has been used. Deep learning methods, which are a type of machine learning algorithm, have emerged in recent years; they have been used for learning or training the complex structures of an actual large-scale dataset continuously collected by sensors in various applications including computer vision, object classification, voice recognition,