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
Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive con...
This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial b...
The electric power system is a complex nonlinear time varying system that needs advanced intelligent...
This paper provides a novel method for nonlinear identification of multiple turbogenerators in a fiv...
This paper presents the design of a continually online trained (COT) artificial neural network (ANN)...
This paper reports on the simulation and practical studies carried out on a single turbogenerator co...
The increasing complexity of a modern power grid highlights the need for advanced system identificat...
The increasing complexity of modern power systems highlights the need for effective system identific...
This paper presents the design of two separate continually online trained (GOT) artificial neural ne...
This paper presents the design of a robust controller for a turbogenerator. The robust controller is...
This paper proposes a new and a novel technique based on Artificial Neural Networks (ANNs) for nonli...
This paper presents the design of two separate continually online trained (COT) neurocontrollers for...
This paper provides a new technique for nonlinear identification of multiple turbogenerators in a fi...
This paper reports on the simulation studies carried out using MATLAB/SIMULINK and the practical imp...
A novel method for nonlinear identification of a static compensator connected to a power system usin...
Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive con...
This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial b...
The electric power system is a complex nonlinear time varying system that needs advanced intelligent...
This paper provides a novel method for nonlinear identification of multiple turbogenerators in a fiv...
This paper presents the design of a continually online trained (COT) artificial neural network (ANN)...
This paper reports on the simulation and practical studies carried out on a single turbogenerator co...
The increasing complexity of a modern power grid highlights the need for advanced system identificat...
The increasing complexity of modern power systems highlights the need for effective system identific...
This paper presents the design of two separate continually online trained (GOT) artificial neural ne...
This paper presents the design of a robust controller for a turbogenerator. The robust controller is...
This paper proposes a new and a novel technique based on Artificial Neural Networks (ANNs) for nonli...
This paper presents the design of two separate continually online trained (COT) neurocontrollers for...
This paper provides a new technique for nonlinear identification of multiple turbogenerators in a fi...
This paper reports on the simulation studies carried out using MATLAB/SIMULINK and the practical imp...
A novel method for nonlinear identification of a static compensator connected to a power system usin...
Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive con...
This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial b...
The electric power system is a complex nonlinear time varying system that needs advanced intelligent...