A novel wind turbine data imputation method with multiple optimizations based on GANs. (May 2020)

• Record Type: Journal Article
• Authors: Qu, Fuming; Liu, Jinhai; Ma, Yanjuan; Zang, Dong; Fu, Mingrui
• Is Part Of: Mechanical systems and signal processing. Volume 139(2020)
• Publication Date: 2020-05
• Subjects: Wind turbine -- Data imputation -- SCADA data -- Multiple optimizations -- Generative adversarial networks; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.106610
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A GANs-based generative model is proposed to impute the missing data of wind turbines. Two optimization functions are proposed to optimize the wind turbine data permutation. Two restrictions are proposed to optimize the training data and the convolutional kernel. Experiments are conducted using the real SCADA data collected from a wind farm. Abstract: In the rising research and applications of data-driven technologies in mechanical systems, data missing has always been a serious problem. The problem of data missing on a large scope has brought grave challenges to the operation and maintenance of the machineries, such as wind turbines (WTs). In this paper, a WT data imputation method with multiple optimizations based on generative adversarial networks (GANs) is proposed. First, to tackle the problem of data missing in large-scale WTs, a conditional GANs-based deep learning generative model is designed according to data features. Second, the permutation of the training data is optimized, so that the convolutional kernel can be better applied. The optimization problem is creatively transformed to a travelling salesman problem (TSP), and two optimization functions are proposed based on data features. Then, the relationship between the training data and the convolutional kernel is studied, and two restrictions are put forward to make the imputation model more effective. Finally, four data imputation experiments and two optimization experiments are carried out using real WT data. The experiment results verify the effectiveness of the proposed method.