Title :
Application of query-based learning to power system static security assessment
Author :
El-Sharkawi, Mohamed A. ; Huang, Steven S.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
A query-based learning and inverted neural network methods are developed for static security assessment of power system. By the proposed method, the demand for huge amounts of data to evaluate the security of the power system can be considerably reduced. The inversion algorithm to generate input patterns at the boundaries of the security region is introduced. The query algorithm is used to enhance the accuracy of the boundaries in the areas where more training data are needed. The IEEE-30 bus system is used to test the proposed method.
Keywords :
learning (artificial intelligence); neural nets; power system computer control; IEEE-30 bus system; inverted neural network methods; power system static security assessment; query-based learning; training; Data security; Neural networks; Power system analysis computing; Power system modeling; Power system security; Power system simulation; Steady-state; System testing; Training data; Voltage;
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.1993.264340
