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Energies 2018, 11(11), 3108; https://doi.org/10.3390/en11113108

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

1
College of Automation, Huaiyin Institute of Technology, Huaian 223003, China
2
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3
College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
*
Authors to whom correspondence should be addressed.
Received: 26 September 2018 / Revised: 30 October 2018 / Accepted: 3 November 2018 / Published: 10 November 2018
(This article belongs to the Section Electrical Power and Energy System)
PDF [1733 KB, uploaded 10 November 2018]

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 pump turbine governing system; model identification; sparse robust least squares support vector machine; synchronous optimization; hybrid backtracking search algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, C.; Li, C.; Peng, T.; Xia, X.; Xue, X.; Fu, W.; Zhou, J. Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm. Energies 2018, 11, 3108.

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