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. ©2010 IEEE.
Recommended Citation
L. L. Grant and G. K. Venayagamoorthy, "Voltage Prediction using a Cellular Network," IEEE PES General Meeting, PES 2010, article no. 5589504, Institute of Electrical and Electronics Engineers, Dec 2010.
The definitive version is available at https://doi.org/10.1109/PES.2010.5589504
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Cellular simultaneous recurrent neural network (CSRN); Small population particle swarm optimization (SPPSO); Voltage profile prediction
International Standard Book Number (ISBN)
978-142448357-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
06 Dec 2010
