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The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

Author

Listed:
  • Yancai Xiao

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Ruolan Dai

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Guangjian Zhang

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Weijia Chen

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.

Suggested Citation

  • Yancai Xiao & Ruolan Dai & Guangjian Zhang & Weijia Chen, 2017. "The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT," Energies, MDPI, vol. 10(11), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1877-:d:119083
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    References listed on IDEAS

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    1. Erik E. Kostandyan & John D. Sørensen, 2011. "Reliability Assessment of Solder Joints in Power Electronic Modules by Crack Damage Model for Wind Turbine Applications," Energies, MDPI, vol. 4(12), pages 1-13, December.
    2. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    3. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
    4. Peng Guo & Nan Bai, 2011. "Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods," Energies, MDPI, vol. 4(11), pages 1-17, November.
    5. Hemmati-Sarapardeh, Abdolhossein & Varamesh, Amir & Husein, Maen M. & Karan, Kunal, 2018. "On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 313-329.
    6. Santos-Alamillos, F.J. & Thomaidis, N.S. & Quesada-Ruiz, S. & Ruiz-Arias, J.A. & Pozo-Vázquez, D., 2016. "Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource?," Renewable Energy, Elsevier, vol. 96(PA), pages 574-582.
    7. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Correction: Liang, Y., et al. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. Energies 2016, 9 , 827," Energies, MDPI, vol. 9(12), pages 1-1, December.
    8. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
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    Cited by:

    1. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2020. "A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells," Applied Energy, Elsevier, vol. 271(C).

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