Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data

Abstract Effective condition monitoring and fault diagnosis of wind turbines are crucial for avoiding serious damages to wind turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides valuable insights into turbine performance. In order to make full use of such valuable information, this paper investigates fault diagnosis of wind turbines by using Gaussian process classifiers (GPC) to the operational data collected from the SCADA system. Both real-time and predictive fault diagnosis were considered. As an alternative to the support vector machine (SVM) technique, the GPC possesses the capability of providing probabilistic information about the fault types, which is valuable for making maintenance plan in real practice. The comparison results show that the GPC method is able to provide more accurate fault diagnosis results than the SVM technique on average.

• Publication year

  2019

• Venue

  Renewable Energy

• Publication date

  2019-04-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/J.RENENE.2018.10.088
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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