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Full-Text Articles in Engineering
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.
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 …
A Continually Online Trained Neurocontroller For The Series Branch Control Of The Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani
A Continually Online Trained Neurocontroller For The Series Branch Control Of The Upfc, Ganesh K. Venayagamoorthy, Radha P. Kalyani
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of a neurocontroller that controls the power flow and regulates voltage along a transmission line. The continually online neurocontroller is used for controlling the series inverter of UPFC. Simulation results carried out in the PSCAD/EMTDC environment are presented …
Computational Enhancement Of Genetic Algorithm Via Control Device Pre-Selection Mechanism For Power System Reactive Power/Voltage Control, G. Krost, Ganesh K. Venayagamoorthy, G. A. Bakare, U. O. Aliyu
Computational Enhancement Of Genetic Algorithm Via Control Device Pre-Selection Mechanism For Power System Reactive Power/Voltage Control, G. Krost, Ganesh K. Venayagamoorthy, G. A. Bakare, U. O. Aliyu
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the application of a novel and computationally enhances genetic algorithm (GA) for solving the reactive power dispatch problem is presented. In order to attain a significant reduction in the computational time of GA, a systematic procedure of reactive power control device pre-selection mechanism is herein proposed to choose a-priori subsets of the available control devices, which maximally influence buses experiencing voltage limit violations. The GA reactive power dispatch module then accesses such judiciously pre-selected control device candidates to determine their optimal settings. A pragmatic scheme aimed at further curtailing the number of the final control actions entertained …
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 …
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 …
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.
Adaptive Critic Designs And Their Implementations On Different Neural Network Architectures, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Designs And Their Implementations On Different Neural Network Architectures, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures.