Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Engineering
Intelligent Integration Of A Wind Farm To An Utility Power Network With Improved Voltage Stability, V. K. Polisetty, Sandhya R. Jetti, Ganesh K. Venayagamoorthy, Ronald G. Harley
Intelligent Integration Of A Wind Farm To An Utility Power Network With Improved Voltage Stability, V. K. Polisetty, Sandhya R. Jetti, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing effect of wind energy generation will influence the dynamic behavior of power systems by interacting with conventional generation and loads. Due to the inherent characteristics of wind turbines, non-uniform power production causes variations in system voltage and frequency. Therefore, a wind farm requires high reactive power compensation. Flexible AC transmission systems (FACTS) devices such as SVCs inject reactive power into the system which helps in maintaining a better voltage profile. This paper presents the design of a linear and a nonlinear coordinating controller between a SVC and the wind farm inverter at the point of interconnection. The performances …
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 …
Adaptive Critic Design Based Neuro-Fuzzy Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Design Based Neuro-Fuzzy Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system …
Intelligent Optimal Control Of Excitation And Turbine Systems In Power Networks, Ganesh K. Venayagamoorthy, Ronald G. Harley
Intelligent Optimal Control Of Excitation And Turbine Systems In Power Networks, 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 and turbine systems. The crucial factors affecting the modern power systems today is voltage control and system stabilization during small and large disturbances. Simulation studies and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system excitation and turbine systems with adaptive and optimal neurocontrol approaches. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances.