Wide-area PMU data monitoring using spatio-temporal statistical models

Abstract

In this paper, a statistically-based, data-driven framework that integrates the use of empirical orthogonal function (EOF) analysis and a time-frequency method is proposed to identify and extract, relevant dynamically independent spatio-temporal patterns from time synchronized data. Using time-frequency methods, the temporal signals at selected system locations are decomposed into modal approximations at different scales. Multi-scale EOF analysis is then used to extract cross-correlations across the measurement sites. The method allows for the extremely nonstationary behavior of interarea oscillations to be analyzed into separate frequency bands and is capable of detecting propagating features in non-stationary processes.

Mode Shape Estimation using Complex Principal Component Analysis and k-Means Clustering

“…Applying the Hilbert Transform requires the input signal to have only one frequency, and that any non-oscillatory trends are subtracted. This can be achieved by using EMD to decompose the input signal into Intrinsic Mode Functions (IMFs) and a Residual [7]. The Hilbert Transform can then be applied to the IMFs, before CPCA is applied on the resulting complex series.…”

“…CPCA is well described in [4], where it is aimed at extracting dynamic patterns in geophysical data sets. Variants of CPCA has been applied to power system analysis in [5]- [7]. The implementation used in this paper is slightly different, but functions in much the same way.…”

Early anomaly detection and localisation in distribution network: a data‐driven approach

“…The data-driven approaches for anomaly analysis in power systems can be broadly categorised into three classes: (1) statistical analysis approaches, (2) signal processing approaches, and (3) artificial intelligence approaches. The statistical approaches often use some statistics for anomaly detection, such as maximum (minimum), mean, variance or higher moments [7][8][9], etc. The signal processing approaches, frequently used in recent years, mainly include Fourier analysis, wavelet transform, principal component analysis (PCA) [10][11][12][13][14][15][16], etc.…”

Power Oscillation Monitoring using Statistical Learning Methods

“…CPCA is described in [6], intended for analysis of geophysical phenomena. Similar work on analysis of electromechanical modes is reported in [7]- [10], where the term Empirical Orthogonal Functions (EOF) is used instead of Principal Component Analysis (PCA). An important contribution from the proposed method described in this paper is the second part, where the observations are clustered.…”