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Articles 31 - 35 of 35
Full-Text Articles in Engineering
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
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.
Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy
Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a technique using artificial neural networks (ANNs) for speaker identification that results in a better success rate compared to other techniques. The technique used in this paper uses both power spectral densities (PSDs) and linear prediction coefficients (LPCs) as feature inputs to a self organizing feature map to achieve a better identification performance. Results for speaker identification with different methods are presented and compared.
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.