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Full-Text Articles in Engineering
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. This paper proposes a stable neural network (NN) controller for the stabilization of a single machine infinite bus power system. In the power system control literature, simplified analytical models are used to represent the power system and the controller designs are not based on rigorous stability analysis. This work overcomes the two major problems by using an accurate analytical model for controller development and presents the closed-loop stability analysis. The NN is used to approximate the …
Supervisory Level Neural Network Identifier For A Small Power System With A Statcom And A Generator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Supervisory Level Neural Network Identifier For A Small Power System With A Statcom And A Generator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A neural network based identifier is designed for effective control of a small power system. The power network in this work is considered from an external point of view, i.e., from a supervisory level. Such a neuroidentifier can serve as a general model of such a plant, and then used for different neural network based control schemes.
New External Neuro-Controller For Series Capacitive Reactance Compensator In A Power Network, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
New External Neuro-Controller For Series Capacitive Reactance Compensator In A Power Network, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The controllable capacitive reactance can be used as the input variable for the external controller of a series capacitive reactance compensator (SCRC) to improve the damping of low-frequency oscillations of the rotor angle and active power in a power system. Conventional linear PI controllers are tuned for best performance at one specific operating point of the nonlinear power system. At other operating point its performance degrades. Nonlinear optimal neuro-controllers are able to overcome this degradation. In this paper, the dual heuristic dynamic programming (DHP) optimization algorithm is applied to design an external nonlinear optimal neuro-controller for the SCRC. Simulation studies …
Neuroidentification Of System Parameters Of The Upfc In A Multimachine Power System, Radha P. Kalyani, Ganesh K. Venayagamoorthy
Neuroidentification Of System Parameters Of The Upfc In A Multimachine Power System, Radha P. Kalyani, Ganesh K. Venayagamoorthy
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 is an effective means for controlling the power flow. The UPFC is controlled conventionally using PI controllers. This paper presents the designs of neuroidentifiers that models the system dynamics one-time step ahead making the pathway for the design of adaptive neurocontrollers. Two neuroidentifiers are used for identifying the nonlinear dynamics of a multimachine power system and UPFC, one neuroidentifier for the shunt inverter and another for the series inverter. Simulation results carried out in the PSCAD/EMTDC environments on multimachine power system are …
Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani
Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem in the discrete-time backstepping design is avoided by using the universal NN approximator. The persistence excitation (PE) condition is relaxed both in the NN observer and …