Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest

doi:10.1016/j.energy.2022.124761

	Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest
	Dong, Mi 3; Sun, Mingren 3; Song, Dongran 3; Huang, Liansheng 2; Yang, Jian 3; Joo, Young Hoon 1
刊名	ENERGY
	2022-10-15
卷号	257
关键词	Model complexity Real-time abnormal detection Semi-supervised learning Wind turbine Model update
ISSN号	0360-5442
DOI	10.1016/j.energy.2022.124761
通讯作者	Song, Dongran(humble_szy@163.com)
英文摘要	Due to extreme weather or wind turbine (WT) fault, WTs often collects abnormal data, which often interferes with the real-time control strategy of WT. To detect the abnormal data in real time, a detection framework suitable for wind power data is proposed, integrating the semi-supervised learning mechanism into the Robust Random Cut Forest algorithm. To do so, the normal data around the wind power curve are firstly selected and used to establish the structure model of normal data, considering the magnitude orders and distribution of different features. In each sample, the new sample data are inserted into the model, of which the complexity change is compared with a dynamic threshold, so as to judge whether the new sample data are abnormal. To reduce the dependence on the selection of the labeled normal data in modelling, it is presented a real-time model updating strategy based on self-training idea in semi-supervised learning. The experimental results show that the detection accuracy of the proposed method can reach 95% with only 1000 groups of the labeled normal data, and the detection time of a single sample is only 50 ms, which can detect abnormal data in real time for facilitating control strategy and other work. (C) 2022 Elsevier Ltd. All rights reserved.
资助项目	National Natural Science Foundation of China[52177204] ; Natural Science Foundation of Hunan Province[2020JJ4744] ; InnovationDriven Project of Central South University[2020CX031] ; National Research Foundation (NRF) - Ministry of Education of South Korea[NRF-2016R1A6A1A03013567]
WOS关键词	ANOMALY DETECTION ; ONLINE DETECTION ; CURVE ; UNCERTAINTY ; TURBINES ; FAULTS ; FARM
WOS研究方向	Thermodynamics ; Energy & Fuels
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000838672700004
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hunan Province ; InnovationDriven Project of Central South University ; National Research Foundation (NRF) - Ministry of Education of South Korea
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/132075]
专题	中国科学院合肥物质科学研究院
通讯作者	Song, Dongran
作者单位	1.Kunsan Natl Univ, Sch IT Informat & Control Engn, Kunsan 54150, South Korea 2.Chinese Acad Sci, Inst Plasma Phys, Hefei 230031, Peoples R China 3.Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
推荐引用方式 GB/T 7714	Dong, Mi,Sun, Mingren,Song, Dongran,et al. Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest[J]. ENERGY,2022,257.
APA	Dong, Mi,Sun, Mingren,Song, Dongran,Huang, Liansheng,Yang, Jian,&Joo, Young Hoon.(2022).Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest.ENERGY,257.
MLA	Dong, Mi,et al."Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest".ENERGY 257(2022).