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Full-Text Articles in Engineering
Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
A review of the applications of intelligent control to replace/augment the conventional excitation and/or turbine control of turbogenerators on the electric power grid is presented in the paper. The intelligent controller designs are based on neural networks and adaptive critic designs (ACDs). The feedback variables are completely based on local measurements from the generators. Simulations and some practical laboratory implementations on a single-machine-infinite-bus and a three-machine power system demonstrate that intelligent controllers are much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. The safety margins on …
Two Separate Continually Online-Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Two Separate Continually Online-Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of two separate continually online trained (COT) neurocontrollers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT artificial neural network is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT neurocontrollers can control turbogenerators under steady-state as well as transient conditions and, thus, allow turbogenerators to operate more closely to their steady-state stability limits
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.
Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of …
Experimental Verification Of Derivatives Adaptive Critic Based Neurocontroller Performance On Single Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Experimental Verification Of Derivatives Adaptive Critic Based Neurocontroller Performance On Single Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design and real-time implementation of derivatives adaptive critic based neurocontrollers that replace the conventional automatic voltage regulators (AVRs) and turbine governors are presented in this paper. The feedback variables to the neurocontroller are completely based on local measurements from the turbogenerator. Experimental verification results are presented to show the superior performance of the derivatives adaptive critic based neurocontroller, compared to the conventional AVR and turbine governor controllers equipped with a power system stabilizer.