DocumentCode :
2378340
Title :
Voltage prediction using a Cellular Network
Author :
Grant, Lisa L. ; Venayagamoorthy, Ganesh Kumar
Author_Institution :
Real-Time Power & Intell. Syst. Lab., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
fYear :
2010
fDate :
25-29 July 2010
Firstpage :
1
Lastpage :
7
Abstract :
Better identification tools are needed for power system voltage profile prediction. The power systems of the future will see an increase in both renewable energy sources and load demand increasing the need for quick estimation of bus voltages and line power flows for system security and contingency analysis. A Cellular Simultaneous Recurrent Neural Network (CSRN) to identify and predict bus voltage dynamics is presented in this paper. The benefit of using a cellular structure over traditional neural network architectures is that the network can represent a direct mapping of any power system allowing for easier scalability to large power systems. A comparison with a standard single SRN is provided to show the advantages of this cellular method. Two types of disturbance are evaluated including perturbations on the power system generators and on the least stable loads. The method is also evaluated for a case involving a transmission line outage.
Keywords :
cellular neural nets; power engineering computing; power system stability; bus voltages; cellular network; cellular simultaneous recurrent neural network; contingency analysis; line power flows; load demand; power system voltage; renewable energy sources; small population particle swarm optimization; system security; voltage prediction; Cellular Simultaneous Recurrent Neural Network (CSRN); Small Population Particle Swarm Optimization (SPPSO); voltage profile prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2010 IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1944-9925
Print_ISBN :
978-1-4244-6549-1
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PES.2010.5589504
Filename :
5589504
Link To Document :
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