In wind turbine, blades are the major component for capturing the wind, however, due to environmental conditions it prompts to get a fault. To overcome this problem, a machine learning based condition monitoring technique is incorporated into the wind turbine to identify the fault classification which occurred in the blade. In this study, a three-bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults mostly affect the turbine blade. In this study, the autoregressive-moving-average (ARMA) features have been extracted from the raw signal and the dominating feature was selected through J48 decision tree algorithm followed by the fault classification using machine learning classifiers. The results were compared with respect to the classification accuracy and their computational time of the classifier. Copyright © 2018 Inderscience Enterprises Ltd.

Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Joshuva A

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

Progress in Industrial Ecology, An International Journal

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. © 2017 ISA

ISA Transactions

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

Fulltext

Indian Journal of Science and Technology

Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm

Structural Durability & Health Monitoring

Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Renewable Energy

Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy

International Journal of Performability Engineering

Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm

Journal	Progress in Industrial Ecology, An International Journal
Publisher	Inderscience Publishers
ISSN	1476-8917
Open Access	No