RESEARCH ON ABNORMAL CONDITION EARLY WARNING FOR WIND TURBINE BASED ON CFSFDP AND LIGHTGBM

被引：0
|
作者：
Ma L. [1 ]
Yuan N. [1 ]
机构：
[1] Department of Automation, North China Electric Power University, Baoding
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 05期
关键词：
abnormal operating condition; CFSFDP; early warning; kernel density estimation; LightGBM; wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2022-0001
中图分类号：
学科分类号：
摘要：
A combination of clustering by fast search and find of density peaks（CFSFDP）and light gradient boosting machine （LightGBM）method is proposed for wind turbine abnormal condition monitoring. Firstly，the CFSFDP algorithm is employed to clean the abnormal condition data and noise data in the supervisory control and data acquisition system（SCADA）. Secondly，by using Bayesian optimization algorithm to search the optimal hyper-parameters of LightGBM，a LightGBM prediction model of wind turbine normal operation condition is established. Aiming at the randomness of wind speed，the time shift sliding window method is adopted to construct the abnormal state identification index and the kernel density estimation is employed to determine its threshold value. Finally，the actual historical faults data of a wind farm are used for experimental verification. The results show the LightGBM_model based early warning approach can warn the abnormal operation conditions of wind turbine timely before a fault occurs. © 2023 Science Press. All rights reserved.
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页码：401 / 406
页数：5
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