Failure warning for offshore wind turbines based on Autoregressive models

Ye, H, Zhu, W, Li, H, Ji, W, Guedes Soares, C and Wang, J (2025) Failure warning for offshore wind turbines based on Autoregressive models. Ocean Engineering, 332. ISSN 0029-8018

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Abstract

In this paper, a failure warning model for offshore wind turbines is constructed based on a hybrid model consisting of the Autoregressive Integrated Moving Average Model (ARIMA), Least Absolute Shrinkage and Selection Operator (LASSO), and multi-condition Exponentially Weighted Moving Average (EWMA) control charts, namely ARIMA-LASSO-EWMA. A correlation analysis is conducted to select variables that are strongly correlated with health state change of wind turbine. The ARIMA model is developed for selected variables and their corresponding residuals are computed. The model is then transferred to LASSO regression to extract the secondary residual as the indicator for the health state recognition. Anomalies in wind turbines are founded when secondary residuals deviate from the normal range. Consequently, an abnormal detection mechanism based on multi-condition EWMA is established, which sets different warning thresholds for various operating conditions of the wind turbines. Failure warning is triggered when the statistic of the secondary residual exceeds the threshold. The results indicate that the proposed failure warning model can provide alarms more than 10 days in advance of actual failure. The proposed model contributes to the condition monitoring, state recognition, and failure warning for offshore wind turbines.

Item Type:	Article
Uncontrolled Keywords:	4007 Control Engineering, Mechatronics and Robotics; 40 Engineering; 7 Affordable and Clean Energy; 0405 Oceanography; 0905 Civil Engineering; 0911 Maritime Engineering; Civil Engineering; 4005 Civil engineering; 4012 Fluid mechanics and thermal engineering; 4015 Maritime engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	1 May 2025
Date Deposited:	13 Jun 2025 14:13
Last Modified:	13 Jun 2025 14:15
DOI or ID number:	10.1016/j.oceaneng.2025.121448
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26589

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