Tingyan Guo

TL;DR: In this article, a two-stage methodology for online identification of power system dynamic signature using phasor measurement unit (PMU) measurements and data mining is proposed, which firstly applies hierarchical clustering to define patterns of unstable dynamic behavior of generators, and then applies different multiclass classification techniques, including decision tree, ensemble decision tree and multiclass support vector machine to identify characterized unstable responses.

TL;DR: In this paper, a generic probabilistic framework for assessing the accuracy of online prediction of power system transient stability based on phasor measurement unit (PMU) measurements and data mining techniques is presented.

TL;DR: In this paper, a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation is proposed, based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation.

TL;DR: The surrogate split method included in the classification and regression tree (CART) algorithm is used to handle the unavailability of measurement signals, and noise present in the on-line data is modeled as white Gaussian noise with various signal-to-noise ratio (SNR).

TL;DR: In this paper, the authors investigated the sensitivity of accuracy of online prediction of power systems transient stability to different uncertainties using Decision Tree (DT) data mining method and demonstrated that DT can predict post-fault system state (stable/unstable) with over 88% accuracy as fast as 0.2 seconds following the fault.