Title :
An adaptive neural network identifier for effective control of a static compensator connected to a power system
Author :
Mohagheghi, Salman ; Park, Jung-Wook ; Harley, Ronald G. ; Venayagamoorthy, Ganesh K. ; Crow, Mariesa L.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.
Keywords :
identification; neurocontrollers; power system control; static VAr compensators; adaptive neural network identifier; adaptive neurocontroller; continually online trained artificial neural networks; power electronic based shunt connected flexible AC transmission system devices; power system; static compensator; Adaptive control; Adaptive systems; Artificial neural networks; Control systems; Neural networks; Power system control; Power system dynamics; Power systems; Programmable control; STATCOM;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1224042
