HEALTH ASSESSMENT OF WIND TURBINE GEARBOX BASED ON CONDITIONAL CONVOLUTION AUTOENCODING GAUSSIAN MIXTURE MODEL

He Qun; Li Yeyang; Jiang Guoqian; Su Nan; Xie Ping; Wu Xin

doi:10.19912/j.0254-0096.tynxb.2022-1239

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 214-220. DOI: 10.19912/j.0254-0096.tynxb.2022-1239

HEALTH ASSESSMENT OF WIND TURBINE GEARBOX BASED ON CONDITIONAL CONVOLUTION AUTOENCODING GAUSSIAN MIXTURE MODEL

He Qun¹, Li Yeyang¹, Jiang Guoqian¹, Su Nan¹, Xie Ping¹, Wu Xin²

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Abstract

To achieve health evaluation and detect wary faults of wind turbine gearboxes, a new conditional convolutional autoencoding Gaussian mixture model is proposed. The sensor information and temporal information are first encoded and decoded at the same time, and the compressed features are extracted. Then, the extracted features are input to the Gaussian mixture model to calculate the energy index based on the probability distribution as the health index for health assessment. Finally, the threshold is determined using the kernel density estimation algorithm. The effectiveness of the proposed method is verified with the supervisory control and data acquisition（SCADA） data from a real wind farm.

Key words

wind turbines / conditional autoencoding network / health assessment / Gaussian mixture model / SCADA

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He Qun, Li Yeyang, Jiang Guoqian, Su Nan, Xie Ping, Wu Xin. HEALTH ASSESSMENT OF WIND TURBINE GEARBOX BASED ON CONDITIONAL CONVOLUTION AUTOENCODING GAUSSIAN MIXTURE MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 214-220 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1239

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