Title :
Application of the ARTMAP neural network to power system stability studies
Author :
Assadi, H. ; Tan, A. ; Etezadi-Amoli, M. ; Egbert, D. ; Fadali, M.S.
Author_Institution :
Dept. of Electr. Eng., Nevada Univ., Reno, NV, USA
Abstract :
The authors discuss the application of a novel variation of the adaptive resonance theory (ART) neural network called fuzzy ARTMAP to the determination of the steady-state stability of a synchronous generator. The model of the generator includes the voltage regulator, the excitor, and the power system stabilizer. The results obtained with the fuzzy ARTMAP network are compared with those obtained with a backpropagation network. For online training, the fuzzy ARTMAP network was found to be a better choice because of its faster convergence, but in some cases, the fuzzy ARTMAP network did not perform as well as the BP network
Keywords :
backpropagation; exciters; neural nets; power system computer control; power system stability; synchronous generators; voltage control; adaptive resonance theory; backpropagation; excitor; fuzzy ARTMAP network; neural network; power system stability; synchronous generator; voltage regulator; Fuzzy neural networks; Neural networks; Power generation; Power system modeling; Power system stability; Resonance; Steady-state; Subspace constraints; Synchronous generators; Voltage;
Conference_Titel :
Systems, Man and Cybernetics, 1992., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0720-8
DOI :
10.1109/ICSMC.1992.271646
