Application of Artificial Neural Networks in Determining Critical Clearing Time in Transient Stability Studies

This paper describes a neural network based adaptive pattern recognition approach by making a thorough analysis on a power system for estimation of the critical clearing time. A nine bus system is considered for the purpose of transient stability analysis. Faults at five locations are assumed at dif...

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Bibliographic Details
Main Authors: Krishna, D.R., Murthy, K., Rao, G.G.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Back propagation
Critical clearing time
Neural Network
Neural networks
Neurons
Pattern analysis
Pattern recognition
Power system analysis computing
Power system faults
Power system stability
Power system transients
Transient Stability
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Summary:This paper describes a neural network based adaptive pattern recognition approach by making a thorough analysis on a power system for estimation of the critical clearing time. A nine bus system is considered for the purpose of transient stability analysis. Faults at five locations are assumed at different instants. Critical clearing times for all five faults at six different loading levels are obtained. Out of thirty cases, 24 cases corresponding to four faults have been used for training the neural network and remaining six CCTs corresponding to the fifth fault at six loading levels obtained by ANN as well as modified Eular method. The same is repeated for all five faults. Nueral network designed with 12 input neurons, 8 hidden neurons and one output neuron. Back propagation technique is used to adjust the weights. Analytical calculations are compared with the values obtained by neural network. Results show that ANN gives accurate results.
DOI:10.1109/ICPST.2008.4745324