How can anomaly detection improve the reliability of wind turbines?

Continuous monitoring of wind turbine health conditions using anomaly detection methods can improve the reliability and reduce maintenance costs during operation of wind turbine. Anomaly detection aims at identifying the root causes leading to unexpected changes of product performance.

How to determine if a wind turbine performance is abnormal?

Anomaly detection with adaptive threshold For deciding whether the performance of a wind turbine is abnormal or not, fixed thresholds were mostly used in the existing methods. Due to the uncertain nature of wind profiles, the operating conditions vary greatly during the operation of wind turbines.

Do wind turbines fail?

Wind turbines are subjected to different sort of failures; thus, before starting to identify various kinds of errors, it is necessary to identify what kind of failures can be found in the real world which causes healthy operation of WTGs.

Can LSTM-sdae and XGBoost detect anomaly in wind turbines?

CONCLUSIONS and future work An anomaly detection and diagnosis method for wind turbines using LSTM-SDAE and XGBoost is proposed in this paper. An abnormal data recognition algorithm based on the LOF and adaptive K-means was first developed to implement data preprocessing and noise extraction.

How do wind turbine condition monitoring and anomaly detection work?

These collected sequential data can reflect the performance conditions of wind turbines. The existing approaches for condition monitoring and anomaly detection of wind turbines belong to either model-based approaches or data-driven approaches (also known as knowledge-based approaches).

Why do wind turbines have a 'normal' performance?

Due to the uncertain nature of wind profiles, the operating conditions vary greatly during the operation of wind turbines. Therefore, the criteria for “normal” performance of wind turbines vary from moment to moment, and the use of fixed thresholds to identify abnormal performance datapoints may result in wrong classifications.