Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids

TL;DR

This paper introduces a novel higher-order motif-based method using MECF for locating forced oscillation sources in power grids, outperforming Fourier analysis and working under various disturbance scenarios.

Contribution

It proposes a new MECF-based unsupervised learning approach for FO source location that requires no prior system knowledge and handles complex oscillation scenarios.

Findings

Effective in locating FO sources in power grids.

Robust against measurement noise and coupling variations.

Outperforms Fourier analysis in diverse FO situations.

Abstract

Time series motifs are used for discovering higher-order structures of time series data. Based on time series motifs, the motif embedding correlation field (MECF) is proposed to characterize higher-order temporal structures of dynamical system time series. A MECF-based unsupervised learning approach is applied in locating the source of the forced oscillation (FO), a periodic disturbance that detrimentally impacts power grids. Locating the FO source is imperative for system stability. Compared with the Fourier analysis, the MECF-based unsupervised learning is applicable under various FO situations, including the single FO, FO with resonance, and multiple sources FOs. The MECF-based unsupervised learning is a data-driven approach without any prior knowledge requirement of system models or typologies. Tests on the UK high-voltage transmission grid illustrate the effectiveness of MECF-based unsupervised learning. In addition, the impacts of coupling strength and measurement noise on locating the FO source by the MECF-based unsupervised learning are investigated.