Title :
Power system reliability evaluation using self organizing map
Author :
Luo, X. ; Singh, C. ; Patton, A.D.
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
Artificial neural networks (ANN) based on the self organizing map (SOM) algorithm has received considerable attention. This paper proposes a new method for power system reliability evaluation by combining Monte Carlo simulation and self organizing map which greatly reduces the computing burden of the loss of load probability calculation compared to Monte Carlo simulation only. A case study of the IEEE RTS system is presented demonstrating the efficiency of this approach
Keywords :
Monte Carlo methods; load flow; power system reliability; power system simulation; self-organising feature maps; IEEE RTS system; Monte Carlo simulation; artificial neural networks; loss of load probability; optimal power flow; power system reliability evaluation; self organizing map; Analytical models; Artificial neural networks; Maintenance; Organizing; Power system analysis computing; Power system modeling; Power system planning; Power system reliability; Power system simulation; Probability;
Conference_Titel :
Power Engineering Society Winter Meeting, 2000. IEEE
Print_ISBN :
0-7803-5935-6
DOI :
10.1109/PESW.2000.850095
