An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system

Li, Chaoshun; Zou, Wen; Zhang, Nan; Lai, Xinjie

doi:10.1016/j.engappai.2017.12.005

Cited by 32 publications

(22 citation statements)

References 49 publications

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“…The speed governor contains a servomechanism and a PID controller to control the guide vane opening. For more details about the four different parts of the PTGS, please refer to Li et al [11] and Zhou et al [37]. The schematic of PTGS is shown in Figure 7 [37].…”

Section: Application To Pump Turbine Governing Systemmentioning

confidence: 99%

“…These models include the ELM, LSSVM, WLSSVM, and S-R-LSSVM models. The input variables are adopted as {u(t − 1), u(t − 2), u(t − 3), y(t − 1), y(t − 2), y(t − 3)} according to Li et al [10,11]. The model parameters are optimized in the same way as the two nonlinear benchmark problems;…”

Section: Application To Pump Turbine Governing Systemmentioning

confidence: 99%

“…The nonlinear system of the PSU is complex in both structure and parameters [8,9]. To achieve an accurate description of the intrinsic characteristics of the PTGS, research on model identification of the PTGS is mainly based on data-driven models such as fuzzy system [3,[10][11][12], neural networks [13,14], grey system theory [15], and the Gaussian mixture model [16].…”

Section: Introductionmentioning

confidence: 99%

“…The novel fuzzy c-regression model clustering algorithm was employed to identify the T-S fuzzy model, and the chaotic gravitational search algorithm was proposed to optimize the parameters of the identification model. In Li et al [11], a new fuzzy membership function was designed for hyperplane-shaped clustering to improve the effectiveness of the T-S fuzzy model identification approach. Yan et al [12] adopted a novel improved hybrid backtracking search algorithm (IHBSA) to partition the T-S fuzzy model clustering space adaptively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Zhang

Tian

et al. 2018

Energies

Self Cite

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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.

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Section: Application To Pump Turbine Governing Systemmentioning

confidence: 99%

Section: Application To Pump Turbine Governing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Tian

et al. 2018

Energies

Self Cite

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show abstract

“…Nonlinear modeling and dynamic analysis of hydraulic turbine governing systems has always been a topic of interest, and there has been much research in this area [11][12][13][14][15][16][17]. There is a consensus that fractional calculus is superior to integer calculus in describing systems with memory, historical dependence, and viscoelasticity [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Design of a Nonlinear Predictive Controller for a Fractional-Order Hydraulic Turbine Governing System with Mechanical Time Delay

et al. 2019

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A nonlinear predictive control method for a fractional-order hydraulic turbine governing system (HTGS) with a time delay is studied in this paper. First, a fractional-order model of a time-delay hydraulic turbine governing system is presented. Second, the fractional-order hydraulic servo subsystem is transformed into a standard controlled autoregressive moving average (CARMA) model according to the Grünwald-Letnikov (G-L) definition of fractional calculus. Third, based on the delayed Takagi-Sugeno fuzzy model, the fuzzy prediction model of the integer-order part of the HTGS is given. Then, by introducing a fourth-order Runge-Kutta algorithm, the fuzzy prediction model can be easily transformed into the CARMA model. Furthermore, a nonlinear predictive controller is proposed to stabilize the time-delay HTGS. Finally, the experiment results are consistent with the theoretical analysis.

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Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia

Telmoudi¹,

Soltani²,

Chaouech³

et al. 2021

Cyberphysical Smart Cities Infrastructures

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An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system

Cited by 32 publications

References 49 publications

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

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

Design of a Nonlinear Predictive Controller for a Fractional-Order Hydraulic Turbine Governing System with Mechanical Time Delay

Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia

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