Title :
Artificial neural networks based steady state equivalents of power systems
Author :
Jilai, Yo ; Zhuo, Liu
Author_Institution :
Dept. of Electr. Eng., Harbin Inst. of Technol., China
Abstract :
The authors propose a new method for artificial neural networks (ANNs) based steady state equivalents of power systems. Because the multilayer Perceptron network (MPN) is a typical ANN and its training algorithm is quite effective, the authors use this network. When the studied power system is divided into three parts, which are internal system (IS), external system (ES) and boundary system (BS). Some tests show that the method has advantages of high accuracy, powerful suitability and high recognition speed
Keywords :
feedforward neural nets; power system computer control; artificial neural networks; boundary system; external system; internal system; multilayer Perceptron network; power systems; steady state equivalents; training algorithm; Artificial neural networks; Biology computing; Multilayer perceptrons; Power system analysis computing; Power system control; Power system planning; Power system security; Power systems; Real time systems; Steady-state;
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
DOI :
10.1109/ANN.1991.213483
