LOAD PREDICTION METHOD OF WIND TURBINE BASED ON MULTISTAGE FEATURE EXTRACTION FRAMEWORK

Yue Jian; Shi Bingshuai; Fan Han; Zhang Ke; Zhang Hailong

doi:10.19912/j.0254-0096.tynxb.2023-1332

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 350-359. DOI: 10.19912/j.0254-0096.tynxb.2023-1332

LOAD PREDICTION METHOD OF WIND TURBINE BASED ON MULTISTAGE FEATURE EXTRACTION FRAMEWORK

Yue Jian, Shi Bingshuai, Fan Han, Zhang Ke, Zhang Hailong

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Abstract

In this paper, load prediction for wind turbines is studied from two aspects： SCADA data enhancement and multi-stage feature extraction framework for load prediction. Firstly, Generative adversarial network （WGAN-GP） is used for data enhancement. In terms of load prediction, different from the traditional Transformer model applied to text data, this paper uses the structured data of wind turbine operation, and in order to improve feature extraction capability, a multistage feature extractor is proposed for feature extraction. Finally, the improved Transformer model is compared with the results of DNN, ResNet and other models, and it is found that the multistage feature extraction model has better prediction effect for data with high correlation with target features, and has better nonlinear extraction ability for data with low correlation.

Key words

wind turbines / Transformer / feature extraction / generative adversarial network / load prediction / data enhancement

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Yue Jian, Shi Bingshuai, Fan Han, Zhang Ke, Zhang Hailong. LOAD PREDICTION METHOD OF WIND TURBINE BASED ON MULTISTAGE FEATURE EXTRACTION FRAMEWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 350-359 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1332

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