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Electrical and Computer Engineering
Electrical and Computer Engineering Faculty Research & Creative Works
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Full-Text Articles in Engineering
Online Identification Of Turbogenerator's Dynamics Using A Neuro-Identifier, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
Online Identification Of Turbogenerator's Dynamics Using A Neuro-Identifier, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of modern power systems highlights the need for effective system identification techniques for the successful control of power system. This paper proposes a robust continually online trained neuroidentifier to predict the outputs of turbogenerator - terminal voltage and speed deviation. The inputs to the neuro-identifier are the changes of the plant's outputs and plant's inputs. It overcomes the drawback of calculating deviation signals from reference signals for different operating points in previous work. Simulation results show that the neuro-identifier can provide accurate identification under different operating conditions. Furthermore, the neuro-identifier can learn the dynamics of the system …
An Adaptive Neural Network Identifier For Effective Control Of A Static Compensator Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Mariesa Crow, Ronald G. Harley
An Adaptive Neural Network Identifier For Effective Control Of A Static Compensator Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Mariesa Crow, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.
Adaptive-Critic-Based Optimal Neurocontrol For Synchronous Generators In A Power System Using Mlp/Rbf Neural Networks, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive-Critic-Based Optimal Neurocontrol For Synchronous Generators In A Power System Using Mlp/Rbf Neural Networks, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design technique is used for the design of the neurocontroller. The performance of the MLPN-based HDP neurocontroller (MHDPC) is compared with the RBFN-based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared …
Adaptive Critic Design Based Neurocontroller For A Statcom Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Design Based Neurocontroller For A Statcom Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A novel nonlinear optimal neurocontroller for a static compensator (STATCOM) connected to a power system using artificial neural networks is presented in this paper. The heuristic dynamic programming (HDP), a member of the adaptive critic designs (ACDs) family, is used for the design of the STATCOM neurocontroller. This neurocontroller provides nonlinear optimal control with better performance compared to the conventional PI controllers.