A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study. (March 2017)

• Record Type: Journal Article
• Authors: Joshuva., A; Sugumaran., V
• Is Part Of: ISA transactions. Volume 67(2017:Mar.)
• Page Start: 160
• Page End: 172
• Publication Date: 2017-03
• Subjects: Fault diagnosis -- Structural health condition monitoring -- Wind turbine blade -- Statistical features -- Best-first tree algorithm -- Functional trees algorithm; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2017.02.002
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. Highlights: Machine learning based condition monitoring is proposed for wind turbine blade. Crack, Erosion, Loose connection, Pitch angle twist and Bend faults are considered. Descriptive statistical features were extracted from the vibration signals. Feature classification was performed using different machine learning classifiers. Functional tree leaves shows the better result of 91.67%.