DocumentCode :
2442944
Title :
Power system network observability determination using feedforward neural networks
Author :
Jain, Amit ; Choi, Jaeho ; Min, Joonki
Author_Institution :
Dept. of Electr. & Comput. Eng., Chung-Buk Nat. Univ., Cheongju, South Korea
Volume :
4
fYear :
2002
fDate :
2002
Firstpage :
2086
Abstract :
This paper presents a new method for the determination of network observability of the power system using the neural networks. The network observability problem, related to the power system configuration or network topology, called the topological observability, is taken for the solution. The topological observability analysis is done utilizing a feedforward neural network (FFNN) model, based on multilayer perceptrons using the backpropagation algorithm. The proposed feed-forward neural network model has been tested on sample power systems and results are presented.
Keywords :
backpropagation; feedforward neural nets; multilayer perceptrons; network topology; observability; power system analysis computing; power system state estimation; back-propagation algorithm; feedforward neural networks; multilayer perceptrons; network topology; power system configuration; power system network observability determination; state estimation; topological observability; Algorithm design and analysis; Feedforward neural networks; Multi-layer neural network; Multilayer perceptrons; Network topology; Neural networks; Observability; Power system analysis computing; Power system modeling; Power systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on
Print_ISBN :
0-7803-7459-2
Type :
conf
DOI :
10.1109/ICPST.2002.1047149
Filename :
1047149
Link To Document :
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