An Approach for Utilizing Correlation among Sensors for Unsupervised Anomaly Detection of Wind Turbine System

Lee, HyunYong; Kim, Nac-Woo; Lee, Jun-Gi; Lee, Byung-Tak

doi:10.1109/ictc52510.2021.9621198

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The reconstruction error zr is measured as the Euclidean distance between x and its decoded counterpart. This is shown in Equation (7).…”

Section: (θ φ; X) = Eq[logpθ(x|z)]-dkl(qφ(z|x) ||Pθ(z)) (6)mentioning

confidence: 99%

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Wind power anomaly data detection based on unsupervised methods

Zhang,

Tang,

et al. 2024

J. Phys.: Conf. Ser.

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During the actual operation of the wind turbine, a large number of abnormal data will be generated due to environmental or human factors, which will have a great impact on the condition assessment and output prediction. In order to make wind energy a reliable source of energy, it is very important to establish an efficient and accurate wind power detection model. Therefore, it becomes essential to identify abnormal data for a more precise evaluation of wind turbine performance. Based on the data mining clustering technology K-means algorithm, this paper introduces an unsupervised abnormal wind power detection algorithm combining the variational autoencoders (VAE) model. The focus of this abnormal wind power detection method is primarily on assessing the reconstruction error, which, in turn, generates an abnormality score for wind power data. This score is then used to determine the presence of abnormal wind power data. Finally, the wind power data in 2021 is tested.

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“…The reconstruction error zr is measured as the Euclidean distance between x and its decoded counterpart. This is shown in Equation (7).…”

Section: (θ φ; X) = Eq[logpθ(x|z)]-dkl(qφ(z|x) ||Pθ(z)) (6)mentioning

confidence: 99%

“…Zhou et al [6] introduced an algorithm that addresses abnormal data in low wind speed and high wind speed categories by combining quartile analysis and clustering techniques for cleanup. HyunYong Lee et al [7] used the correlation between sensors to construct an unsupervised anomaly detection model for wind turbine systems.…”

Section: Introductionmentioning

confidence: 99%