Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers
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 specif...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/5389574 |
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| author | Bikash Chandra Saha Joshuva Arockia Dhanraj M. Sujatha R. Vallikannu Mohana Alanazi Ahmad Almadhor Ravishankar Sathyamurthy Kuma Gowwomsa Erko V. Sugumaran |
| author_facet | Bikash Chandra Saha Joshuva Arockia Dhanraj M. Sujatha R. Vallikannu Mohana Alanazi Ahmad Almadhor Ravishankar Sathyamurthy Kuma Gowwomsa Erko V. Sugumaran |
| author_sort | Bikash Chandra Saha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8985e548dbf7413ab0e27e21b45ff88f |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-8985e548dbf7413ab0e27e21b45ff88f2025-02-03T05:53:29ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/5389574Investigating Rotor Conditions on Wind Turbines Using Integrating Tree ClassifiersBikash Chandra Saha0Joshuva Arockia Dhanraj1M. Sujatha2R. Vallikannu3Mohana Alanazi4Ahmad Almadhor5Ravishankar Sathyamurthy6Kuma Gowwomsa Erko7V. Sugumaran8Department of Electrical and Electronics EngineeringCentre for Automation and Robotics (ANRO)Department of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electrical EngineeringDepartment of Computer Engineering and NetworksDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringSchool of Mechanical EngineeringRenewable 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.http://dx.doi.org/10.1155/2022/5389574 |
| spellingShingle | Bikash Chandra Saha Joshuva Arockia Dhanraj M. Sujatha R. Vallikannu Mohana Alanazi Ahmad Almadhor Ravishankar Sathyamurthy Kuma Gowwomsa Erko V. Sugumaran Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers International Journal of Photoenergy |
| title | Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers |
| title_full | Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers |
| title_fullStr | Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers |
| title_full_unstemmed | Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers |
| title_short | Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers |
| title_sort | investigating rotor conditions on wind turbines using integrating tree classifiers |
| url | http://dx.doi.org/10.1155/2022/5389574 |
| work_keys_str_mv | AT bikashchandrasaha investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT joshuvaarockiadhanraj investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT msujatha investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT rvallikannu investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT mohanaalanazi investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT ahmadalmadhor investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT ravishankarsathyamurthy investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT kumagowwomsaerko investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers AT vsugumaran investigatingrotorconditionsonwindturbinesusingintegratingtreeclassifiers |