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Cost-sensitive large margin distribution machine for fault detection of wind turbines

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Abstract

Given the importance of the class-imbalanced data and misclassified unequal costs in large wind turbine datasets, this paper proposes a cost-sensitive large margin distribution machine (CLDM) for fault detection of wind turbines. The margin mean and margin variance are use to characterize the margin distribution. The objective function and constraints of the large margin distribution machine (LDM) are modified to be cost-sensitive. The class-imbalanced data and misclassified unequal costs are solved by selecting the appropriately cost-sensitive parameters. Then CLDM is designed to train and test data from wind turbines in a wind farm. In order to verify the effectiveness of CLDM, it is compared with support vector machine (SVM), cost-sensitive SVM, and LDM. Comprehensive experiments on 7 datasets from a benchmark model of wind turbines and 5 datasets from a real wind farm show that CLDM has better sensitivity, gMean and average misclassified cost than the other methods.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (# 61403046, # 61463009, # 51674042),the Major Program of the National Natural Science Foundation of China under Grant (# 61490702), the Natural Science Foundation of Hunan Province, China (# 2015JJ3005), China Scholarship Council, the Key Laboratory of Renewable Energy Electric-Technology of Hunan Province, the Key Laboratory of Efficient & Clean Energy Utilization of Hunan Province, Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Open Project Funding of Fujian Key Laboratory of Information Processing and Intelligent Control (MJUKF201737), and Hunan Province 2011 Collaborative Innovation Center of Clean Energy and Smart Grid.

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Authors and Affiliations

  1. School of Energy and Power Engineering, Changsha University of Science & Engineering, Changsha, 410114, China

    Mingzhu Tang & Daifei Liu

  2. Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Duisburg, 47057, Germany

    Mingzhu Tang & Steven X. Ding

  3. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Mingzhu Tang & Chunhua Yang

  4. College of Electrical Engineering, Aanhui Politechnical University, Wuhu, 241000, China

    Fanyong Cheng

  5. Department of Chemical Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Yuri A. W. Shardt

  6. Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou, 550004, China

    Wen Long

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  1. Mingzhu Tang
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Correspondence to Mingzhu Tang.

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Tang, M., Ding, S.X., Yang, C. et al. Cost-sensitive large margin distribution machine for fault detection of wind turbines. Cluster Comput 22 (Suppl 3), 7525–7537 (2019). https://doi.org/10.1007/s10586-018-1854-3

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