Title :
Load flow solution in electrical power systems with variable configurations by progressive learning networks
Author :
Augugliaro, A. ; Cataliotti, V. ; Dusonchet, L. ; Favuzza, S. ; Scaccianoce, G.
Author_Institution :
Palermo Univ., Italy
Abstract :
In recent years, interest in the application of soft computing techniques to electrical power systems has rapidly grown; in particular the application of artificial neural networks (ANN) and genetic algorithms (GA) in the solution of load-flow problem in wide electrical power systems, as valid alternative to the classical numerical algorithms, is an interesting research topic. In the present paper, a refined solution strategy based on statistical methods, on a particular the grouping genetic algorithm (GGA) and on progressive learning networks (PLN) is presented to solve load-flow problems in electrical power systems taking also into account configuration changes; in particular, a procedure to solve the system when a link is removed, or added, is described and implemented. Test results on the standard IEEE 118 bus network have demonstrated the good potential and efficiency of the procedure.
Keywords :
genetic algorithms; learning (artificial intelligence); load flow; power system analysis computing; IEEE 118 bus network; artificial neural networks; computer simulation; configuration changes; genetic algorithms; grouping genetic algorithm; power system load flow solution; progressive learning networks; statistical methods; variable configurations; Artificial neural networks; Computer applications; Genetic algorithms; Load flow; Load flow analysis; Neural networks; Power system analysis computing; Power systems; Testing; Voltage;
Conference_Titel :
Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on
Conference_Location :
Budapest, Hungary
Print_ISBN :
0-7803-5836-8
DOI :
10.1109/PTC.1999.826562
