Forced Oscillation Identification and Filtering from Multi-Channel Time-Frequency Representation

TL;DR

This paper presents a systematic multi-channel time-frequency analysis method for identifying and filtering non-stationary forced oscillations in power systems, aiding in locating oscillation sources even under resonance conditions.

Contribution

It introduces a novel framework combining ridge estimation and filtering on TFRs, and compares three TF approaches for improved FO source localization.

Findings

The method effectively identifies oscillation frequencies in simulated and real signals.

Filtering on the TF plane enhances the accuracy of FO source location.

Comparison shows the advantages of the proposed approach over traditional methods.

Abstract

Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially in cases under resonance condition with natural system modes, where the magnitudes of the oscillations could be greater in places far from the source. Therefore, it is of interest to construct a global time-frequency (TF) representation (TFR) of the system, which can capture the oscillatory components present in the system. In this paper we develop a systematic methodology for frequency identification and component filtering of non-stationary power system forced oscillations (FO) based on multi-channel TFR. The frequencies of the oscillatory components are identified on the TF plane by applying a modified ridge estimation algorithm. Then, filtering of the components is carried out on the TF plane applying the anti-transform functions over the individual TFRs around the identified ridges. This step constitutes an initial stage for the application of the Dissipating Energy Flow (DEF) method used to locate FO sources. Besides, we compare three TF approaches: short-time Fourier transform (STFT), STFT-based synchrosqueezing transform (FSST) and second order FSST (FSST2). Simulated signals and signals from real operation are used to show that the proposed method provides a systematic framework for identification and filtering of power systems non-stationary forced oscillations.