Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers

Author:

Saha Bikash Chandra1,Dhanraj Joshuva Arockia2,Sujatha M.3,Vallikannu R.4,Alanazi Mohana5,Almadhor Ahmad6,Sathyamurthy Ravishankar7,Erko Kuma Gowwomsa8ORCID,Sugumaran V.9

Affiliation:

1. Department of Electrical and Electronics Engineering, Cambridge Institute of Technology, Ranchi, Jharkhand 835103, India

2. Centre for Automation and Robotics (ANRO), Department of Mechatronics Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, 603103 Tamil Nadu, India

3. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Vijayawada, Andhra Pradesh 522502, India

4. Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Padur, Kelambakkam, Tamil Nadu 603103, India

5. Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 42421, Saudi Arabia

6. Department of Computer Engineering and Networks, Jouf University, Sakaka 42421, Saudi Arabia

7. Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, 641407 Tamil Nadu, India

8. Department of Mechanical Engineering, Ambo University, Ethiopia

9. School of Mechanical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India

Abstract

Renewable wind power is productive and feasible to manage the energy crisis and global warming. The wind turbine’s blades are the essential components. The dimension of wind turbine blades has been increased with blade sizes varying from approx. 25 m up to approx. 100 m or even greater with a specific purpose to increase energy efficiency. While wind turbine blades tend to be highly stressed by environmental conditions, the wind turbine blade must be constantly tested, inspected, and monitored for wind turbine blades safety monitoring. This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. Five defects were reported for the diagnosis study of defective wind turbine rotor blades, and the considered defects are blade crack, erosion, loose hub blade contact, angle twist, and blade bend. The statistical features have been drawn from the recorded vibration signals, and the important features was selected through J48 classifier. Eight tree-dependent classifiers were used to categorize the state of the rotor blades. Among the classifiers, the least absolute deviation tree performed better with the classification percentage of 90% ( Kappa statistics = 0.88 , MAE = 0.0362 , and RMSE = 0.1704 ) with a computational time of 0.06 s.

Publisher

Hindawi Limited

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,Atomic and Molecular Physics, and Optics,General Chemistry

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3