A data mining framework for online dynamic security assessment: Decision trees, boosting, and complexity analysis

Online dynamic security assessment provides the real-time situational awareness for assessing the impact of various N-k contingencies, so that appropriate preventive/corrective controls could be armed in a timely fashion. This task is challenging due to the large number of possible contingencies, the massive scale of power systems, and the multi-scale dynamics that occur under varying operating conditions. In this study, a data mining framework for online dynamic security assessment using decision trees and a boosting technique is developed, with the following multi-stage processing. 1) In the offline training stage, classifiers consisting of multiple simple decision trees are built based on a given collection of training data, and an iterative algorithm is used to “boost” the accuracy of the classifiers. 2) In the near real-time update stage, the simple decision trees together with their voting weights are updated when new data are available, enabling a smooth tracking of the changes of decision regions. 3) In the online DSA stage, real-time phasor measurements are used to locate the current operating condition into a decision region and obtain timely security decisions. The clustering of contingencies and data preprocessing via dimension reduction of the attributes are also discussed. Numerical testing based on a practical power system demonstrates that the proposed approach works well under a variety of realistic operating conditions.

[1]  Johan F. Hoorn,et al.  Situational awareness , 2006, Cognition, Technology & Work.

[2]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[3]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Peter Buhlmann,et al.  BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING , 2007, 0804.2752.

[6]  Jianzhong Tong,et al.  On-line transient stability screening of 14,000-bus models using TEPCO-BCU: Evaluations and methods , 2010, IEEE PES General Meeting.

[7]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[8]  Ruisheng Diao,et al.  Design of a Real-Time Security Assessment Tool for Situational Awareness Enhancement in Modern Power Systems , 2010, IEEE Transactions on Power Systems.

[9]  Paul E. Utgoff,et al.  Decision Tree Induction Based on Efficient Tree Restructuring , 1997, Machine Learning.

[10]  Peter L. Bartlett,et al.  Boosting Algorithms as Gradient Descent , 1999, NIPS.

[11]  Vijay Vittal,et al.  An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees , 2007 .

[12]  Ruisheng Diao,et al.  Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements , 2009, IEEE Transactions on Power Systems.

[13]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.