Title :
Transient stability assessment using artificial neural network
Author_Institution :
Granherne Int. Ltd., Doha, Qatar
Abstract :
This paper proposes a novel method of transient stability assessment by adaptive pattern recognition which makes use of an artificial neural network (ANN). A multilayer feed forward network has been applied to a sample nine bus three machine system. A test set pattern chosen outside the region of the learning set patterns giving very close to desirable output (error below 5%) validates the generalising and extrapolating capability of the network after learning. The method has been successfully applied to determine which particular machine goes out of step.
Keywords :
Lyapunov methods; adaptive control; backpropagation; fault location; feedforward neural nets; pattern recognition; power system analysis computing; power system faults; power system transient stability; Lyapunov methods; adaptive pattern recognition; artificial neural network; backpropagation; extrapolating capability; fault location; multilayer feed forward network; nine bus three machine system; pattern set learning; power system transient stability; Artificial neural networks; Biological neural networks; Fault location; Lyapunov method; Neurons; Pattern recognition; Power system stability; Power system transients; Stability analysis; Testing;
Conference_Titel :
Electric Utility Deregulation, Restructuring and Power Technologies, 2004. (DRPT 2004). Proceedings of the 2004 IEEE International Conference on
Print_ISBN :
0-7803-8237-4
DOI :
10.1109/DRPT.2004.1338060