Title :
Constrained power system state estimation on recurrent neural networks
Author_Institution :
Russian Acad. of Sci., Syktyvkar
Abstract :
A new method for power system state estimation which combines robust M-estimation with treatment of the inequality constraints is presented. The main advantage of the method is that most expensive computation is performed in a neural network which is amenable to parallel implementation. The designed recurrent neural networks are based on differential equations and realize searching a saddle point for appropriate Lagrangian function. Test results on standard test system are used to illustrate the effectiveness of the method.
Keywords :
differential equations; neural nets; power engineering computing; power system state estimation; Lagrangian function; constrained power system state estimation; differential equations; recurrent neural networks; robust M-estimation; Computer networks; Concurrent computing; Differential equations; Lagrangian functions; Neural networks; Power systems; Recurrent neural networks; Robustness; State estimation; System testing; power system state estimation; recurrent neural network; robustness;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524438
