Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

Yan Pei, Zheng Qian, Bo Jing, Dahai Kang and Lizhong Zhang
Additional contact information
Yan Pei: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China
Zheng Qian: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China
Bo Jing: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China
Dahai Kang: Beijing Power Concord Technology Co. Ltd. Beijing 100048, China
Lizhong Zhang: Beijing Power Concord Technology Co. Ltd. Beijing 100048, China

Energies, 2018, vol. 11, issue 3, 1-14

Abstract: Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA) data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.

Keywords: data-driven method; wind turbine; yaw angle; zero-point shifting; fault detection (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/3/553/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/3/553/ (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:11:y:2018:i:3:p:553-:d:134721

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 ().