An Extended Kalman Filter Enhanced Hilbert-Huang Transform in Oscillation Detection
Abstract
Hilbert-Huang transform (HHT) has drawn great attention in power system analysis due to its capability to deal with dynamic signal and provide instantaneous characteristics such as frequency, damping, and amplitudes. However, its shortcomings, including mode mixing and end effects, are as significant as its advantages. A preliminary result of an extended Kalman filter (EKF) method to enhance HHT and hopefully to overcome these disadvantages is presented in this paper. The proposal first removes dynamic DC components in signals using empirical mode decomposition. Then an EKF model is applied to extract instant coefficients. Numerical results using simulated and real-world low-frequency oscillation data suggest the proposal can help to overcome the mode mixing and end effects with a properly chosen number of modes.
Cite
@article{arxiv.1711.04644,
title = {An Extended Kalman Filter Enhanced Hilbert-Huang Transform in Oscillation Detection},
author = {Zhe Yu and Di Shi and Haifeng Li and Yishen Wang and Zhehan Yi and Zhiwei Wang},
journal= {arXiv preprint arXiv:1711.04644},
year = {2017}
}
Comments
5 pages, 2 figures. Submitted to 2018 IEEE PES General Meeting. arXiv admin note: text overlap with arXiv:1706.05355