State identification and fault early warning of wind turbine based on MSET

Yingzhe Zhang; Qiancheng Zhao; Sheng He; Xian Wang

doi:10.1117/12.2612026

19 November 2021 State identification and fault early warning of wind turbine based on MSET

Yingzhe Zhang, Qiancheng Zhao, Sheng He, Xian Wang

Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 1205911 (2021) https://doi.org/10.1117/12.2612026
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China

Abstract

In recent years, with the development of wind power industry, the installed capacity is increasing rapidly, and the installation sites are developing towards the ocean and remote mountainous areas, which makes the maintenance of wind turbines difficult and the cost increases. In order to reduce the maintenance cost and improve the maintenance efficiency, and ensure the economic and reliable operation of the equipment, many state recognition and fault early warning methods have been proposed one after another, and the intelligent fault diagnosis method based on supervisory control and data acquisition(SCADA) data and various kinds of machine learning has gradually become a research hotspot. In this paper, the generator system of a wind turbine in a wind farm is taken as the research object. By using the massive SCADA data recorded in operation, a multivariate state estimation technique (MSET) is established to predict specific operation parameters with relevant operation parameters as input. This method uses clustering algorithm to clean up the selected SCADA data, then uses MSET to establish the prediction model, and calculates the prediction residual by sliding window method to realize the fault diagnosis. Finally, the effectiveness of the method is verified by actual SCADA data

Citation Download Citation

Yingzhe Zhang, Qiancheng Zhao, Sheng He, and Xian Wang "State identification and fault early warning of wind turbine based on MSET", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 1205911 (19 November 2021); https://doi.org/10.1117/12.2612026

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available