Excitation and Turbine Neurocontrol with Derivative Adaptive Critics of Multiple Generators on the Power Grid

Ganesh K. Venayagamoorthy; Donald C. Wunsch; Ronald G. Harley

doi:10.1109/IJCNN.2001.939494

Excitation and Turbine Neurocontrol with Derivative Adaptive Critics of Multiple Generators on the Power Grid

Ganesh K. Venayagamoorthy
, Donald C. Wunsch
, Ronald G. Harley

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances

Original language	American English
Journal	Proceedings of the International Joint Conference on Neural Networks, 2001. IJCNN '01
DOIs	https://doi.org/10.1109/IJCNN.2001.939494
State	Published - Jan 1 2001

Keywords

Derivative Adaptive Critics
Dynamics
Excitation
Heuristics
Learning (Artificial Intelligence)
Learning Algorithm
Neurocontrollers
Optimisation
Power System Control
Power System Stability
Stability
Three-Machine Power System
Turbine Generator

Disciplines

Electrical and Computer Engineering

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10.1109/IJCNN.2001.939494License: Unspecified

Excitation and Turbine Neurocontrol with Derivative Adaptive CritFinal published version, 559 KBLicense: Unspecified

Cite this

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title = "Excitation and Turbine Neurocontrol with Derivative Adaptive Critics of Multiple Generators on the Power Grid",

abstract = " Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances",

keywords = "Derivative Adaptive Critics, Dynamics, Excitation, Heuristics, Learning (Artificial Intelligence), Learning Algorithm, Neurocontrollers, Optimisation, Power System Control, Power System Stability, Stability, Three-Machine Power System, Turbine Generator",

author = "Venayagamoorthy, \{Ganesh K.\} and Wunsch, \{Donald C.\} and Harley, \{Ronald G.\}",

year = "2001",

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language = "American English",

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AU - Harley, Ronald G.

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Y1 - 2001/1/1

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AB - Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances

KW - Derivative Adaptive Critics

KW - Dynamics

KW - Excitation

KW - Heuristics

KW - Learning (Artificial Intelligence)

KW - Learning Algorithm

KW - Neurocontrollers

KW - Optimisation

KW - Power System Control

KW - Power System Stability

KW - Stability

KW - Three-Machine Power System

KW - Turbine Generator

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/1609

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DO - 10.1109/IJCNN.2001.939494

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JF - Proceedings of the International Joint Conference on Neural Networks, 2001. IJCNN '01

ER -

Excitation and Turbine Neurocontrol with Derivative Adaptive Critics of Multiple Generators on the Power Grid

Abstract

Keywords

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