Condition monitoring and fault diagnosis of wind turbine: a systematic literature review

Wind energy penetration has considerably increased in the recent past. However, wind turbines are often prone to various faults which may lead to failures causing huge production and economic losses with increased downtime. To reduce this production and economic loss. It is therefore clear that early detection of these failures can be achieved through an appropriate condition monitoring approach. Various approaches are reported for predicting the condition of wind turbines. However, deploying a costly condition monitoring system with additional data accusation devices poses a challenge for windfarm owners. To address this challenge this study employing Preferred Reporting Item for Systematic Literature Review and Meta Analysis (PRISMA) provides a detailed review of various approaches used for the wind turbine condition monitoring. The key objective of this study is to find out the most frequently used and reliable method of wind turbine condition monitoring, focusing particularly on the SCADA-based approach due to its practical advantages and widespread adoption in the industry. Additionally, this review considers the distinctive concept of machine learning model building which includes data input and its processing, feature selection, model building and its evaluation to analyze the research issues. The review findings concluded that amongst various condition monitoring techniques, SCADA based data driven approach is most popular as it does not require additional sensors, blade mount cameras, unmanned arial vehicles and a separate data accusation unit. Nevertheless, condition monitoring results based on SCADA approach to provide varying predications for differently located wind farms which is a pertinent knowledge gap. This review study provides some detailed insight into various condition monitoring approaches of wind turbines and recommendation to consider any of these based on available resources.<br>