Title :
Radial basis function networks for contingency analysis of bulk power systems
Author :
Refaee, J.A. ; Mohandes, M. ; Maghrabi, H.
Author_Institution :
Dept. of Electr. Eng. & Res. Inst., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fDate :
5/1/1999 12:00:00 AM
Abstract :
Radial basis function networks (RBFNs) are used for contingency evaluation of bulk power system. The motivation behind this work is to exploit the nonlinear mapping capabilities of RBFN in estimating line loading and bus voltage of a bulk power system following a contingency. Unlike most of the available neural networks based techniques, the proposed method utilizes the potential of RBFN in planning studies. The performance of the RBFN is compared with a standard AC load flow algorithm
Keywords :
load flow; power system analysis computing; power system parameter estimation; power system planning; radial basis function networks; AC power flow; bulk power systems; bus voltage estimation; contingency analysis; line loading estimation; nonlinear mapping capabilities; planning; radial basis function networks; standard AC load flow algorithm; Load flow; Neural networks; Neurons; Power engineering and energy; Power generation; Power system analysis computing; Power system modeling; Power system planning; Radial basis function networks; Voltage;
Journal_Title :
Power Systems, IEEE Transactions on
