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Full-Text Articles in Engineering
Novel Dynamic Representation And Control Of Power Systems With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Novel Dynamic Representation And Control Of Power Systems With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
FACTS devices have been shown to be useful in damping power system oscillations. However, in large power systems, the FACTS control design is complex due to the combination of differential and algebraic equations required to model the power system. In this paper, a new method to generate a nonlinear dynamic representation of the power network is introduced to enable more sophisticated control design. Once the new representation is obtained, a back stepping methodology for the UPFC is utilized to mitigate the generator oscillations. Finally, the neural network approximation property is utilized to relax the need for knowledge of the power …
Novel Dynamic Representation And Control Of Power Networks Embedded With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Novel Dynamic Representation And Control Of Power Networks Embedded With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
FACTS devices have been shown to be powerful in damping power system oscillations caused by faults; however, in the multi machine control using FACTS, the control problem involves solving differential-algebraic equations of a power network which renders the available control schemes ineffective due to heuristic design and lack of know how to incorporate FACTS into the network. A method to generate nonlinear dynamic representation of a power system consisting of differential equations alone with universal power flow controller (UPFC) is introduced since differential equations are typically preferred for controller development. Subsequently, backstepping methodology is utilized to reduce the generator oscillations …
Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes
Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents two neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control signals are calculated using local signals only, the transient and overall system stabilities can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system and the interconnection terms, thus the requirements for exact system parameters are released. Simulation studies with a three machine power system demonstrate the effectiveness of the proposed controller designs.
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Automating the task of nanomanipulation is extremely important since it is tedious for humans. This paper proposes an atomic force microscope (AFM) based force controller to push nano particles on the substrates. A block phase correlation-based algorithm is embedded into the controller for the compensation of the thermal drift which is considered as the main external uncertainty during nanomanipulation. Then, the interactive forces and dynamics between the tip and the particle, particle and the substrate are modeled and analyzed. Further, an adaptive critic NN controller based on adaptive dynamic programming algorithm is designed and the task of pushing nano particles …
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 …
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.