Small hydro power plant identification using NNARX structure

Kishor, Nand; Saini, Priyanka; Singh, Pravendra

doi:10.1007/s00521-004-0456-6

Cited by 16 publications

(6 citation statements)

References 22 publications

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“…Supposing that h v has a nonlinear relation characterized by (24) and shown in Figure 2 Considering that the system dynamics is unknown, the first stage in the work plan will be to obtain a NN model of…”

Section: Equilibrium States Of a Tunnel-diode Circuit Let The Tunnelmentioning

confidence: 99%

“…Proper selection and training of a basic structure such as a multilayer perceptron (MLP) can accurately reproduce the behaviour of a nonlinear system. This modelling technique can be used, both qualitatively and analytically [6,[22][23][24][25], taking into account that MLPs are universal approximators, either for a function [26][27][28][29] or its derivative [30,31]. Thus, although the system might be unknown, it is possible to obtain a NN model of its behaviour, representing its dynamics in the workspace studied.…”

Section: Introductionmentioning

confidence: 99%

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About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

et al. 2018

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This work presents a straightforward methodology based on neural networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of multilayer perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian matrix of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour.

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Section: Equilibrium States Of a Tunnel-diode Circuit Let The Tunnelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

et al. 2018

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“…Due to its powerful nonlinear fitting and forecasting ability, the neural networks technique has been widely used in the modeling and identification of various nonlinear systems [13,17]. The main process of the model identification of a hydroelectric generating unit or PSU based on neural networks is optimizing the weights and biases of each neuron in the network with the help of training algorithms such as back-propagation, gradient descent, and intelligent optimization algorithms, and then, the nonlinear characteristics of HTGS or PTGS can be effectively described using the identification model.…”

Section: Introductionmentioning

confidence: 99%

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

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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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“…Both the methods have been shown to possess the capability of modifying complex nonlinear processes to the arbitrary degree of accuracy. In recent publications (Kishor et al, 2005(Kishor et al, , 2006Kishor and Singh, 2007a,b), NNs-based method (i.e. NNARX model) has been successfully shown to represent the hydro plant dynamics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive intelligent hydro turbine speed identification with water and random load disturbances

Kishor

Singh

Raghuvanshi

2007

Engineering Applications of Artificial Intelligence

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Small hydro power plant identification using NNARX structure

Cited by 16 publications

References 22 publications

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

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

Adaptive intelligent hydro turbine speed identification with water and random load disturbances

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