Title :
Determination of operational parameters of electrical machines using evolutionary programming
Author :
Ma, J.T. ; Lai, L.L.
Author_Institution :
City Univ., London, UK
Abstract :
This paper presents an artificial intelligence approach of using evolutionary programming to estimate the transient and subtransient parameters of a generator under normal operation. The estimation using evolutionary programming is compared with that using corrected extended Kalman filter. The comparison shows that evolutionary programming is robust to search the real values of parameters even when the data are highly contaminated by noises, while with extended Kalman filter, the estimation tends to diverge with such data
Keywords :
Kalman filters; artificial intelligence; electric generators; filtering theory; genetic algorithms; machine theory; parameter estimation; simulated annealing; corrected extended Kalman filter; electrical machines; evolutionary programming; generators; noise contaminated data; operational parameters determination; subtransient parameters; transient parameters;
Conference_Titel :
Electrical Machines and Drives, 1995. Seventh International Conference on (Conf. Publ. No. 412)
Conference_Location :
Durham
Print_ISBN :
0-85296-648-2
DOI :
10.1049/cp:19950846
