基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究

doi:10.3969/j.issn.1000-1158.2024.09.17

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Abstract A wind turbine fault warning method based on quartile method(QM)-density-based spatial clustering of applications with noise(DBSCAN) and Bi-directional long and short-term memory network (BiLSTM) is proposed.Firstly, in view of the difficulty of cleaning the power limit point in the wind speed-power diagram, the combination of QM and DBSCAN is proposed to preprocess the modeling operation data. secondly, by analyzing the operation principle of wind turbine and determining the input and output parameters of the normal working condition prediction model of wind turbine combined with LightGBM feature selection method, a high-precision normal performance prediction model of wind turbine is established based on BiLSTM.Then, the state performance index of the fan is determined by the sliding window algorithm, and the index threshold is determined by statistical interval estimation method.Finally, the real fault data of the fan is used to carry out the early warning experiment of the abnormal working condition of the whole wind turbine, which verifies the effectiveness of the method.

Key words： electrical measurement wind turbine fault warning quartile method DBSCAN BiLSTM sliding window algorithm

Received: 31 May 2023 Published: 26 September 2024

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TB971

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	Articles by authors
	MA Liangyu
	LIANG Shuyuan
	CHENG Dongyan
	GENG Yanzhu
	DUAN Xinhui

Cite this article:

MA Liangyu,LIANG Shuyuan,CHENG Dongyan, et al. Research on Early Warning of Abnormal Working Conditions of Wind Turbine Based on QM-DBSCAN and BiLSTM[J]. Acta Metrologica Sinica, 2024, 45(9): 1384-1393.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.09.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I9/1384

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