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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate the …
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Electrical and Computer Engineering Faculty Research & Creative Works
The development and implementation of the optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) is presented. This ondemand routing protocol minimizes a novel link cost factor which is defined using available energy, end-to-end (E2E) delay and distance from a node to the base station (BS), along with clustering, to effectively route information to the BS. Initially, the nodes are either in idle or sleep mode, but once an event is detected, the nodes near the event become active and start forming sub-networks. Formation of the inactive network into a sub-network saves energy because only a portion of …
Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani
Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In power system controls, simplified analytical models are used to represent the dynamics of power system and controller designs are not rigorous with no stability analysis. One reason is because the power systems are complex nonlinear systems which pose difficulty for analysis. This paper presents a feedback linearization based power system stabilizer design for a single machine infinite bus power system. Since practical operating conditions require the magnitude of control signal to be within certain limits, the stability of the control system under control limits is also analyzed. Simulation results under different kinds of operating conditions show that the controller …
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.