Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining


This paper proposes a 2-stage methodology for online identification of Power System dynamic signature using phasor measurement unit (PMU) measurements and Data Mining. Only transient stability standing is usually predicted within the literature to assist with corrective control, without the dynamic behavior of generators within the event of instability. This paper uses ancient binary classification to spot transient stability in the first stage, and then develops a unique methodology to predict the character of unstable dynamic behavior in the second stage. The method firstly applies hierarchical clustering to define patterns of unstable dynamic behavior of generators, and then applies different multiclass classification techniques, as well as call tree, ensemble call tree and multiclass support vector machine to identify characterized unstable responses. The proposed methodology is demonstrated on a multi-area transmission check system. High prediction accuracy at both stages of identification is demonstrated.

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