An Extended Kalman Filter Enhanced Hilbert-Huang Transform in Oscillation Detection

TL;DR

This paper introduces an enhanced Hilbert-Huang Transform method using an Extended Kalman Filter to improve oscillation detection in power systems by addressing mode mixing and end effects.

Contribution

It combines empirical mode decomposition with an EKF to mitigate HHT's limitations, offering a novel approach for more accurate oscillation analysis.

Findings

Improved mode separation in simulated data

Effective reduction of end effects in real-world data

Enhanced detection accuracy of low-frequency oscillations

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.