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Full-Text Articles in Engineering
Optimal Dynamic Neurocontrol Of A Gate-Controlled Series Capacitor In A Multi-Machine Power System, Swakshar Ray, Ganesh K. Venayagamoorthy, Edson H. Watanabe, F. D. De Jesus
Optimal Dynamic Neurocontrol Of A Gate-Controlled Series Capacitor In A Multi-Machine Power System, Swakshar Ray, Ganesh K. Venayagamoorthy, Edson H. Watanabe, F. D. De Jesus
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances.
Adaptive Critic Designs For Optimal Control Of Power Systems, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Designs For Optimal Control Of Power Systems, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for …
Two Separate Continually Online-Trained Neurocontrollers For A Unified Power Flow Controller, Ganesh K. Venayagamoorthy, Radha P. Kalyani
Two Separate Continually Online-Trained Neurocontrollers For A Unified Power Flow Controller, 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 neurocontrollers to provide better damping during transient and dynamic control. Two separate neurocontrollers are used for controlling the UPFC, one neurocontroller for the shunt inverter and the other for the series inverter. Simulation studies carried out in …
Mlp/Rbf Neural-Networks-Based Online Global Model Identification Of Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Mlp/Rbf Neural-Networks-Based Online Global Model Identification Of 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 neural network (MLPN) and a radial basis function neural network (RBFN) for online identification of the nonlinear dynamics of a synchronous generator in a power system. The computational requirement to process the data during the online training, local convergence, and online global convergence properties are investigated by time-domain simulations. The performances of the identifiers as a global model, which are trained at different stable operating conditions, are compared using the actual signals as well as the deviation signals for the inputs of the identifiers. Such an online-trained identifier with fixed optimal …
A Neural Network Based Wide Area Monitor For A Power System, Xiaomeng Li, Ganesh K. Venayagamoorthy
A Neural Network Based Wide Area Monitor For A Power System, Xiaomeng Li, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an …