Adaptive fault detection in wind turbine via RF and CUSUM. Issue 10 (3rd July 2020)

• Record Type: Journal Article
• Authors: Xu, Qifa; Lu, Shixiang; Zhai, Zhongping; Jiang, Cuixia
• Is Part Of: IET renewable power generation. Volume 14:Issue 10(2020)
• Page Start: 1789
• Page End: 1796
• Publication Date: 2020-07-03
• Subjects: maintenance engineering -- wind power plants -- data acquisition -- control charts -- fault diagnosis -- statistical process control -- wind turbines; wind turbine -- RF -- wind industry -- heavy monitoring tasks -- highly recommended method -- fault record information -- efficient normal behaviour modelling -- dynamic residual monitoring -- novel adaptive fault detection scheme -- random forest -- adaptive cumulative sum -- nonlinear mechanism -- target variable -- adaptive CUSUM -- control chart -- time‐varying shift -- supervisory control -- data acquisition data -- wind farm -- faults -- reducing false alarms; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2019.0913
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Rapid developments of wind industry arise the issue of heavy monitoring tasks. The residual monitoring based on normal behaviour modelling is a highly recommended method when fault record information is missing. However, it is difficult to achieve efficient normal behaviour modelling and dynamic residual monitoring simultaneously. To this end, a novel adaptive fault detection scheme, which merges random forest (RF) with adaptive cumulative sum (CUSUM), is proposed. The authors exploit RF to explore the non‐linear mechanism between features and the target variable robustly, and obtain the residuals quickly. Then, they design the adaptive CUSUM control chart of time‐varying shift to sensitively detect the changes of residuals. For illustration, they apply the proposed scheme to the supervisory control and data acquisition data acquired from a wind farm in China. The empirical results demonstrate that the proposed scheme is superior to several competing methods in capturing faults and reducing false alarms. Meanwhile, the authors find it can detect anomaly quickly, automatically and robustly under different signal‐to‐noise ratios. These provide operators sufficient time to adopt an effective maintenance strategy.