Title :
A hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach
Author :
Chang, C.S. ; Srinivasan, Dipti ; Liew, A.C., Sr.
Author_Institution :
Dept. of Electr. Eng., Singapore Polytech., Singapore
fDate :
2/1/1994 12:00:00 AM
Abstract :
A methodology for evaluation of transient stability of medium size interconnected longitudinal power systems has been developed using a hybrid neural network pattern recognition approach. Assessment of transient stability is done using a fast pattern recognition algorithm at each load level, accurately predicted by a neural network on a half-hourly basis. As opposed to the conventional approaches, this hybrid strategy can make fast decisions with less computations
Keywords :
feedforward neural nets; load forecasting; pattern recognition; power system analysis computing; power system interconnection; power system stability; power system transients; feedforward neural nets; hybrid model; interconnected longitudinal power systems; load forecasting; neural network; pattern recognition; security transfer limits; transient stability evaluation; Hybrid power systems; Load forecasting; Neural networks; Pattern recognition; Power system interconnection; Power system modeling; Power system security; Power system stability; Power system transients; Weather forecasting;
Journal_Title :
Power Systems, IEEE Transactions on
