Title :
Contingency analysis of bulk power system using neural networks
Author :
Maghrabi, H. ; Refaee, J.A. ; Mohandes, M.
Author_Institution :
Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
Abstract :
Radial basis function networks (RBFNs) are used for the contingency evaluation of bulk power systems. The motivation behind this work is to exploit the nonlinear mapping capabilities of RBFN in estimating line loading and bus voltages of a bulk power system following a contingency. Unlike most of the available neural network-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 planning; radial basis function networks; bulk power system contingency analysis; bus voltages; computational performance; computer simulation; contingency evaluation; line loading; neural networks; nonlinear mapping capabilities; planning studies; radial basis function networks; standard AC load flow algorithm; Artificial neural networks; Load forecasting; Neural networks; Neurons; Power system analysis computing; Power system planning; Power system reliability; Power systems; Transfer functions; Voltage;
Conference_Titel :
Power System Technology, 1998. Proceedings. POWERCON '98. 1998 International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4754-4
DOI :
10.1109/ICPST.1998.729286
