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Full-Text Articles in Electrical and Computer 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 …
Real-Time Dual Heuristic Programming-Based Neurocontroller For A Turbogenerator In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Real-Time Dual Heuristic Programming-Based Neurocontroller For A Turbogenerator In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances.
A Heuristic Dynamic Programming Based Power System Stabilizer For A Turbogenerator In A Single Machine Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
A Heuristic Dynamic Programming Based Power System Stabilizer For A Turbogenerator In A Single Machine Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power …
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of nonlinear optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP-based neurocontrol is much more effective than the conventional proportional-integral-derivative control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …
Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller's training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in …
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.
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.
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 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 …
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 …
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …
Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator …
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 (GOT) artificial neural network (ANN) controllers 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 ANN is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT ANN controllers can control turbogenerators under steady state as well as transient conditions and thus allow turbogenerators to operate more closely to their steady state stability …
Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For 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 a neurocontroller for a turbogenerator that augments/replaces the conventional automatic voltage regulator and the turbine governor. The neurocontroller uses a novel technique based on the adaptive critic designs with emphasis on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results are presented to show that the DHP based neurocontroller is robust and performs better than the HDP based neurocontroller, as well as the conventional controller, especially when the system conditions and configuration changes.
Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Turbogenerators are nonlinear time varying systems. This paper presents the design of a neurocontroller for such a turbogenerator that augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor using a novel technique based on the adaptive critic designs (ACDs) with emphasis on global dual heuristic programming (GDHP). Simulation results are presented to show that the neurocontroller derived with the GDHP approach is robust and its performance is better when compared with that derived with other neural network technique, especially when system conditions and configuration changes.
Experimental Studies With A Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Experimental Studies With A Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a continually online trained (COT) artificial neural network (ANN) controller for a laboratory turbogenerator system connected to the infinite bus through a transmission line in real time. Two COT ANNs are used for the implementation: one ANN to identify the complex nonlinear dynamics of the power system, and the other ANN to control the turbogenerator. Practical results are presented to show that COT ANN controllers can control turbogenerators under steady state as well as transient conditions in the laboratory environment
A Continually Online Trained Artificial Neural Network Identifier For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Continually Online Trained Artificial Neural Network Identifier For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of modern power systems highlights the need for advanced modelling techniques for effective control of power systems. This paper presents results of simulation and practical studies carried out on identifying the dynamics of a single turbogenerator connected to an infinite bus through a short transmission line, using a continually online trained (COT) artificial neural network (ANN).
A Robust Artificial Neural Network Controller For A Turbogenerator When Line Configuration Changes, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Robust Artificial Neural Network Controller For A Turbogenerator When Line Configuration Changes, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a robust controller for a turbogenerator. The robust controller is an artificial neural network (ANN) that is trained offline on a family of ANN models of the turbogenerator. This ANN controller augments/replaces the traditional automatic voltage controller (AVR) and the turbine governor of the generator. Simulation results are presented to show that the ANN controller is robust when the transmission line configuration changes.
Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a neuro-controller for a turbogenerator using a novel technique based on adaptive critic designs (ACD). This adaptive critic design based neuro-controller augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor of the generator. Simulation results are presented to show that neural network controllers with the ACD have the potential to control turbogenerators when system conditions and configuration changes.
Implementation Of An Adaptive Neural Network Identifier For Effective Control Of Turbogenerators, Ganesh K. Venayagamoorthy, Ronald G. Harley
Implementation Of An Adaptive Neural Network Identifier For Effective Control Of Turbogenerators, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented.
A Practical Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Practical Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper reports on the simulation and practical studies carried out on a single turbogenerator connected to an infinite bus through a short transmission line, with a continually online trained (COT) artificial neural network (ANN) controller to identify the turbogenerator, and another COT ANN to control the turbogenerator. This identifier/controller augments/replaces the automatic voltage regulator and the turbine governor. Results are presented to show that this COT ANN identifier/controller has the potential to allow turbogenerators to operate more closely to their steady-state stability limits and nevertheless “ride through” severe transient disturbances such as three phase faults. This allows greater usage …