TY - GEN
T1 - Iteratively Reweighted Linear Least Squares for Frequency Estimation in Unbalanced Three-phase Power System
AU - Chen, Yuan
AU - Sun, Weize
AU - Huang, Long-Ting
AU - So, Hing Cheung
PY - 2019/5
Y1 - 2019/5
N2 - Smart grid has attracted increasing attention in the past decade, and one of its common problems is the variation of the nominal frequency (50 or 60 Hz) introduced by harmonics. In this paper, a batch-mode frequency estimator that can accurately obtain the deviation from the nominal frequency is proposed. The signal model, which includes not only the fundamental frequency but also the harmonics, is first defined, and its characteristic is then studied. Employing the linear prediction (LP) property of the model, the deviated frequency is iteratively updated according to the weighted LP errors, to achieve accurate fundamental frequency estimation. Computer simulations indicate that our proposed method is more accurate and reliable than the conventional estimators in the presence of harmonics and amplitude oscillation.
AB - Smart grid has attracted increasing attention in the past decade, and one of its common problems is the variation of the nominal frequency (50 or 60 Hz) introduced by harmonics. In this paper, a batch-mode frequency estimator that can accurately obtain the deviation from the nominal frequency is proposed. The signal model, which includes not only the fundamental frequency but also the harmonics, is first defined, and its characteristic is then studied. Employing the linear prediction (LP) property of the model, the deviated frequency is iteratively updated according to the weighted LP errors, to achieve accurate fundamental frequency estimation. Computer simulations indicate that our proposed method is more accurate and reliable than the conventional estimators in the presence of harmonics and amplitude oscillation.
KW - batch-mode
KW - frequency estimation
KW - generalized weighted linear prediction
KW - harmonics
KW - Unbalanced three-phase power system
UR - https://www.scopus.com/pages/publications/85068982717
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85068982717&origin=recordpage
U2 - 10.1109/ICASSP.2019.8682364
DO - 10.1109/ICASSP.2019.8682364
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479981328
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8097
EP - 8101
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing
PB - IEEE
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)
Y2 - 12 May 2019 through 17 May 2019
ER -