Determination of operational parameters of electrical machines using evolutionary programming

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