Estimating Health Condition of the Wind Turbine Drivetrain System

Peng Qian, Xiandong Ma and Dahai Zhang
Additional contact information
Peng Qian: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK
Xiandong Ma: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK
Dahai Zhang: Ocean College, Zhejiang University, Hangzhou 310058, China

Energies, 2017, vol. 10, issue 10, 1-19

Abstract: Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.

Keywords: condition monitoring; online sequential extreme learning machine (OS-ELM); Bonferroni interval; health condition; drivetrain; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/10/1583/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/10/1583/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:10:p:1583-:d:114752

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().