DocumentCode :
2945581
Title :
Artificial neural networks based steady state security analysis of power systems
Author :
Shukla, Meera ; Abdelrahman, Mohamed
Author_Institution :
Dept. of Electr. & Comput. Eng., Tennessee Technol. Univ., Cookeville, TN, USA
fYear :
2004
fDate :
2004
Firstpage :
266
Lastpage :
269
Abstract :
The focus of this paper is to present an artificial neural network based methodology to assess the steady state security of a power system. The security of the system is assessed on the basis of the voltage profile at each bus with reference to changes in generation and load in the system. The input to the neural network is the voltage level at each bus. The ANN used is a feedforward multilayer network trained with a backpropagation algorithm. The output of the ANN classifies the security of the power system into normal, alert and emergency states. An IEEE 14-bus system is considered to demonstrate the results of the methodology.
Keywords :
backpropagation; electric power generation; feedforward neural nets; multilayer perceptrons; network topology; power system security; artificial neural networks; backpropagation algorithm; feedforward multilayer network; multilayered perceptrons; neural network topology; power generation; power systems; static security; steady state security analysis; voltage level; Artificial neural networks; Information security; Load flow; Power engineering and energy; Power engineering computing; Power system analysis computing; Power system reliability; Power system security; Steady-state; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 2004. Proceedings of the Thirty-Sixth Southeastern Symposium on
ISSN :
0094-2898
Print_ISBN :
0-7803-8281-1
Type :
conf
DOI :
10.1109/SSST.2004.1295661
Filename :
1295661
Link To Document :
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