Abstract
Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as $k$ -Nearest Neighbor ( $k{\text{NN}}$ ) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
Highlights
W IDE-AREA monitoring of power systems plays a crucial role in understanding the system behavior and improving the system operating stability margin
With more and more advanced measuring devices such as Phasor Measurement Units (PMUs) spreading across power systems, abundant measurements containing the information of the system operating status are available for analysis
The existing data-driven methods can be divided into three categories according to the applications: (1) for the protection of power system equipment, e.g., the wavelet coefficient energy based method [3] and the hidden Markov model based method [4]; (2) for the analysis of power quality especially the waveform of alternate voltage, e.g., the HilbertHuang transform based method [5] and the power quality state estimation based method [6]; (3) for the assessment of the system security and stability, typically by multivariate statistical analysis based methods [7]–[10]
Summary
W IDE-AREA monitoring of power systems plays a crucial role in understanding the system behavior and improving the system operating stability margin. The method presented in [19] operates in a univariate manner to analyze variables separately and the online computational burden increases with the number of variables increasing Against this background, the motivation of this work is to integrate kNN with the PCA-based statistical monitoring method in order that a large number of variables can be analyzed in real time for wide-area monitoring of power systems, and at the same time, the masking effect of the oscillatory trends and noise in CAI et al.: WIDE-AREA MONITORING OF POWER SYSTEMS USING PRINCIPAL COMPONENT ANALYSIS electrical measurements on disturbances can be reduced. The transpose and inverse operators are denoted by (·)T and (·)−1 respectively
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