Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation

Title & Authors
Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation
Karami, Ali;

Abstract
This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin($\small{{\Delta}V_n}$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the $\small{{\Delta}V_n}$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.
Keywords
Energy margin;PEBS method;Radial basis function neural network;Transient stability;
Language
English
Cited by
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Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network,;

Journal of Electrical Engineering and Technology, 2014. vol.9. 1, pp.293-300
1.
Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network, Journal of Electrical Engineering and Technology, 2014, 9, 1, 293
2.
Transient Stability Analysis of a Two-Machine Power System under Different Fault Clearing Times, International Journal of Electronics and Electrical Engineering, 2014, 3, 1
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