Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm

Chu Zhang, Chaoshun Li, Tian Peng, Xin Xia, Xiaoming Xue, Wenlong Fu and Jianzhong Zhou
Additional contact information
Chu Zhang: College of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Chaoshun Li: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tian Peng: College of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Xin Xia: College of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Xiaoming Xue: College of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Wenlong Fu: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Jianzhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2018, vol. 11, issue 11, 1-21

Abstract: In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid backtracking search algorithm for the model identification of a pumped turbine governing system. By introducing the maximum linearly independent set, the sparsity of the support vectors of the LSSVM model are realized, and the complexity is reduced. The robustness of the identification model to noise and outliers is enhanced using the weighted function based on improved normal distribution. In order to further improve the accuracy and generalization performance of the sparse robust LSSVM identification model, the model input variables, the kernel parameters, and the regularization parameters are optimized synchronously using a binary-real coded backtracking search algorithm. Experiments on two benchmark problems and a real-world application of a pumped turbine governing system in a pumped storage power station in China show that the proposed sparse robust LSSVM model optimized by the hybrid backtracking search algorithm can not only obtain higher identification accuracy, it also has better robustness and a higher generalization performance compared with the other existing models.

Keywords: pump turbine governing system; model identification; sparse robust least squares support vector machine; synchronous optimization; hybrid backtracking search algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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