A novel wind turbine condition monitoring method based on cloud computing

Peng Qian, Dahai Zhang, Xiange Tian, Yulin Si and Liangbi Li

Renewable Energy, 2019, vol. 135, issue C, 390-398

Abstract: With the development of condition monitoring technology, the data collected by sensors are voluminous and much faster than before. The cloud computing technology is a good solution for big data processing, it is therefore very suitable to be applied in the condition monitoring of the wind turbine, especially for data-driven model-based condition monitoring methods. In order to solve this problem, a novel wind turbine condition monitoring method based on cloud computing is proposed in this paper. A data-driven model-based condition monitoring (CM) method by using hierarchical extreme learning machine (H-ELM) algorithm is adopted to achieve fault detection of the gearbox in the wind turbine, which has better performance than traditional ELM method. Then, compressed sensing (CS) method is applied to compress the first hidden layer output that will be uploaded to the cloud for further calculation. The proposed method is not only able to detect the faults effectively, but also considering data upload quantity reduction and data security. The case study validates the effectiveness of the proposed method. Consequently, it is effective and can also enhance economic benefit and operating efficiency of the wind farm.

Keywords: Cloud computing; Wind turbine; Data security; SCADA data; Hierarchical extreme learning machine; Compressed sensing (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:135:y:2019:i:c:p:390-398

DOI: 10.1016/j.renene.2018.12.045

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