Title :
Transient stability evaluation using an artificial neural network (power systems)
Author :
Omata, Kazuya ; Tanomura, Kenichi
Author_Institution :
Toshiba Corp., Tokyo, Japan
Abstract :
This paper describes a power system transient stability evaluation method using an artificial neural network (ANN). To improve the accuracy of the evaluation, the authors propose a new type of training signal which is a reciprocal of the action time of a step-out relay (SOR) after the fault occurrence. In simulation results of a 16-bus system, the evaluation accuracy of the ANN trained using the proposed training signal is about 20 percent more accurate than that of an ANN trained using the conventional 0/1 digital signal.
Keywords :
electrical faults; learning (artificial intelligence); neural nets; power system analysis computing; power system stability; AI; accuracy; artificial neural network; fault; power system analysis computing; step-out relay; training signal; transient stability evaluation; Artificial neural networks; Performance evaluation; Power system faults; Power system measurements; Power system modeling; Power system relaying; Power system simulation; Power system stability; Power system transients; Signal generators;
Conference_Titel :
Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
Conference_Location :
Yokohama, Japan
Print_ISBN :
0-7803-1217-1
DOI :
10.1109/ANN.1993.264301