Title :
Comparison of adaptive filter performance in estimating the noise statistics for harmonic models
Author :
Yaz, E. ; Gao, Y. ; Olejniczak, K.
Author_Institution :
Dept. of Electr. Eng., Arkansas Univ., Fayetteville, AR, USA
Abstract :
Electric power system harmonics and interharmonics having random variations in magnitude are modeled as state variables of a discrete-time stochastic system. The problem of adaptively estimating the statistical characteristics of the process and measurement noises is addressed. Incorporating the unknown noise covariance in the system leads to a state-multiplicative-noise model resulting in a nonlinear estimation problem. The main contribution of the paper is the performance comparison, by Monte Carlo simulation, between the linear minimum-variance filter and the extended Kalman filter. Conclusions based on these simulation results are presented
Keywords :
Monte Carlo methods; adaptive Kalman filters; adaptive estimation; discrete time systems; filtering theory; noise; power system harmonics; state estimation; stochastic systems; Monte Carlo simulation; discrete-time stochastic system; extended Kalman filter; harmonic models; interharmonics; linear minimum-variance filter; measurement noise; noise statistics; performance comparison; power system harmonics; process noise; statistical characteristics; unknown noise covariance; Active filters; Adaptive filters; Frequency; Nonlinear filters; Power harmonic filters; Power system harmonics; Power system modeling; Semiconductor device noise; State estimation; Statistics;
Conference_Titel :
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5250-5
DOI :
10.1109/CDC.1999.833354