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Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

Yuhan Liu, Yuqiao Zheng*, Zhuang Ma, Cang Wu

School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding Author: Yuqiao Zheng. Email: email

Energy Engineering 2023, 120(5), 1223-1237. https://doi.org/10.32604/ee.2023.026329

Abstract

An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition (SCADA) normal data drive. Firstly, the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted. Then the spindle speed is employed as the output parameter, and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the pre-processed data, with the evaluation indexes selected as the optimal model. Finally, calculating the spindle operation status index according to the sliding window principle, ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event, analyzing the 2.5 MW wind turbine SCADA data from a domestic wind field as a sample, The results show that the fault warning time of the early warning model is 5.7 h ahead of the actual fault occurrence time, as well as the identification and early warning of abnormal wind turbine spindle operation without abnormal data or a priori knowledge of related faults.

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Cite This Article

Liu, Y., Zheng, Y., Ma, Z., Wu, C. (2023). Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data. Energy Engineering, 120(5), 1223–1237.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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