Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

Yichuan Fu*, Zhiwei Gao, Aihua Zhang, Xiaoxu Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728143958
DOIs
Publication statusPublished - 2021
Event19th IEEE International Conference on Industrial Informatics, INDIN 2021 - Mallorca, Spain
Duration: 21 Jul 202123 Jul 2021

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2021-July
ISSN (Print)1935-4576

Conference

Conference19th IEEE International Conference on Industrial Informatics, INDIN 2021
Country/TerritorySpain
CityMallorca
Period21/07/2123/07/21

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