DocumentCode :
1000685
Title :
Two separate continually online-trained neurocontrollers for a unified power flow controller
Author :
Venayagamoorthy, Ganesh K. ; Kalyani, Radha P.
Author_Institution :
Real-Time Power & Intelligent Syst. Lab., Univ. of Missouri, Rolla, MO, USA
Volume :
41
Issue :
4
fYear :
2005
Firstpage :
906
Lastpage :
916
Abstract :
The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of neurocontrollers to provide better damping during transient and dynamic control. Two separate neurocontrollers are used for controlling the UPFC, one neurocontroller for the shunt inverter and the other for the series inverter. Simulation studies carried out in the PSCAD/EMTDC environment is described and results show the successful control of the UPFC and the power system with two neurocontrollers. Performances of the neurocontrollers are compared with the conventional proportional plus integral controllers for system oscillation damping under different operating conditions for large disturbances.
Keywords :
PI control; control engineering computing; invertors; load flow control; neurocontrollers; oscillations; power system CAD; power system control; power system faults; power system transient stability; EMTDC; PSCAD; load flow control; online trained neurocontrollers; power network transient stability; proportional plus integral controllers; shunt inverter; system oscillation damping; unified power flow controller; Load flow; Neurocontrollers; PSCAD; Power system control; Power system dynamics; Power system modeling; Power system simulation; Power system stability; Power system transients; Power systems; Indirect adaptive control; Unified Power Flow Controller (UPFC); neurocontrollers; neuroidentifiers; power system;
fLanguage :
English
Journal_Title :
Industry Applications, IEEE Transactions on
Publisher :
ieee
ISSN :
0093-9994
Type :
jour
DOI :
10.1109/TIA.2005.851571
Filename :
1468266
Link To Document :
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