Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k{\text{NN}}$</tex-math> </inline-formula> ) 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.