4.7 Article

Wind turbine condition monitoring by the approach of SCADA data analysis

期刊

RENEWABLE ENERGY
卷 53, 期 -, 页码 365-376

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2012.11.030

关键词

Condition monitoring; Wind turbine; SCADA

资金

  1. European FP7 Project OPTIMUS [322430]
  2. China Natural Science Foundation [51075331]
  3. EPSRC Supergen Wind Energy Technologies Consortium [EP/D034566/2]

向作者/读者索取更多资源

Wind turbines are being increasingly deployed in remote onshore and offshore areas due to the richer wind resource there and the advantages of mitigating the land use and visual impact issues. However, site accessing difficulties and the shortage of proper transportation and installation vehicles/vessels are challenging the operation and maintenance of the giants erected at these remote sites. In addition to the continual pressure on lowering the cost of energy of wind, condition monitoring is being regarded as one of the best solutions for the maintenance issues and therefore is attracting significant interest today. Much effort has been made in developing wind turbine condition monitoring systems and inventing dedicated condition monitoring technologies. However, the high cost and the various capability limitations of available achievements have delayed their extensive use. A cost-effective and reliable wind turbine condition monitoring technique is still sought for today. The purpose of this paper is to develop such a technique through interpreting the SCADA data collected from wind turbines, which have already been collected but have long been ignored due to lack of appropriate data interpretation tools. The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational conditions. Both laboratory and site verification tests have been conducted. It has been shown that the proposed technique not only has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration. (C) 2012 Elsevier Ltd. All rights reserved.

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