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Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Failure analysis; Fault detection; Gaussian distribution; Global warming; Learning algorithms; Support vector machines; Wind power; Cost-efficient; Faults diagnosis; Gaussians; Harmful gas; Machine learning techniques; Performance; Renewable energies; Support vectors machine; Wind turbine faults; Wi...

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Main Authors: Binti Shahrulhisham N.N.H., Chong K.H., Yaw C.T., Koh S.P.
Other Authors: 57884510200
Format: Conference Paper
Published: Institute of Physics 2023
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spelling my.uniten.dspace-271262023-05-29T17:39:55Z Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis Binti Shahrulhisham N.N.H. Chong K.H. Yaw C.T. Koh S.P. 57884510200 36994481200 36560884300 57883863700 Failure analysis; Fault detection; Gaussian distribution; Global warming; Learning algorithms; Support vector machines; Wind power; Cost-efficient; Faults diagnosis; Gaussians; Harmful gas; Machine learning techniques; Performance; Renewable energies; Support vectors machine; Wind turbine faults; Wind turbine systems; Wind turbines Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems. � Published under licence by IOP Publishing Ltd. Final 2023-05-29T09:39:54Z 2023-05-29T09:39:54Z 2022 Conference Paper 10.1088/1742-6596/2319/1/012017 2-s2.0-85137684452 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137684452&doi=10.1088%2f1742-6596%2f2319%2f1%2f012017&partnerID=40&md5=10e925bb05953de1e873a5ef48a0f01e https://irepository.uniten.edu.my/handle/123456789/27126 2319 1 12017 All Open Access, Gold Institute of Physics Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Failure analysis; Fault detection; Gaussian distribution; Global warming; Learning algorithms; Support vector machines; Wind power; Cost-efficient; Faults diagnosis; Gaussians; Harmful gas; Machine learning techniques; Performance; Renewable energies; Support vectors machine; Wind turbine faults; Wind turbine systems; Wind turbines
author2 57884510200
author_facet 57884510200
Binti Shahrulhisham N.N.H.
Chong K.H.
Yaw C.T.
Koh S.P.
format Conference Paper
author Binti Shahrulhisham N.N.H.
Chong K.H.
Yaw C.T.
Koh S.P.
spellingShingle Binti Shahrulhisham N.N.H.
Chong K.H.
Yaw C.T.
Koh S.P.
Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
author_sort Binti Shahrulhisham N.N.H.
title Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
title_short Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
title_full Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
title_fullStr Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
title_full_unstemmed Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
title_sort application of machine learning technique using support vector machine in wind turbine fault diagnosis
publisher Institute of Physics
publishDate 2023
_version_ 1806426467874111488
score 13.222552

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