Wind turbine performance degradation monitoring using DPGMM and Mahalanobis distance

Peng Guo, Yu Gan and David Infield

Renewable Energy, 2022, vol. 200, issue C, 1-9

Abstract: Real-time monitoring of wind turbine performance degradation can improve the economics and safety of wind farms. Normal operational data can accurately reflect the generation performance of a wind turbine and in the wind-speed coordinate system these normal data constitute the “main power band”. This paper invokes a Dirichlet Process Gaussian Mixture Model (DPGMM) to cluster operational data in each horizontal power bin, and the number of Gaussian components can be determined automatically. The confidence ellipses of Gaussian components can be used to identify the contour of the main power band which is then used as baseline performance model. In the monitoring phase, Mahalanobis distance is used to judge whether new monitoring data lies outside the contour of main power band and thus should be labeled as degraded operational data. When the proportion of such data exceeds a set value in a sliding window, a wind turbine performance degradation alarm is triggered. Degradation degree and rate can quantitatively measure the severity of performance degradation. For an industrial performance degradation case caused by gearbox oil over temperature, the method proposed timely gives alarm only 12 points (2 h) later than the first degraded operational data appears and is proved to be effective.

Keywords: Wind turbine; Performance degradation; Dirichlet process Gaussian mixture model (DPGMM); Mahalanobis distance (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:200:y:2022:i:c:p:1-9

DOI: 10.1016/j.renene.2022.09.115

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