An overview of decision tree applied to power systems

The corrosive volume of available data in electric power systems motivates the adoption of data mining techniques in the emerging field of power system data analytics. The mainstream of data mining algorithm applied to power system, decision tree (DT), also named as classification and regression tree (CART), has gained increasing interests because of its high performance in terms of computational efficiency, uncertainty manageability, and interpretability. The fundamental knowledge of CART algorithm is introduced in this paper, followed by an overview of a variety of DT applications to power systems for better interfacing power systems with data analytics.

[1]  K. A. Papadogiannis,et al.  Optimal allocation of primary reserve services in energy markets , 2004, IEEE Transactions on Power Systems.

[2]  J. Friedman Stochastic gradient boosting , 2002 .

[3]  Ruisheng Diao,et al.  Decision tree assisted controlled islanding for preventing cascading events , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[4]  Hiroyuki Mori,et al.  Short-term load forecasting with fuzzy regression tree in power systems , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[5]  C. W. Taylor,et al.  Decision trees using apparent resistance to detect impending loss of synchronism , 2000 .

[6]  Louis Wehenkel Machine-Learning Approaches to Power-System Security Assessment , 1997, IEEE Expert.

[7]  Ruisheng Diao,et al.  Decision Tree-Based Preventive and Corrective Control Applications for Dynamic Security Enhancement in Power Systems , 2010, IEEE Transactions on Power Systems.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Geza Joos,et al.  On the Accuracy Versus Transparency Trade-Off of Data-Mining Models for Fast-Response PMU-Based Catastrophe Predictors , 2012, IEEE Transactions on Smart Grid.

[10]  Hiroyuki Mori,et al.  Optimal regression tree based rule discovery for short-term load forecasting , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[11]  Subhransu Ranjan Samantaray,et al.  Decision tree-initialised fuzzy rule-based approach for power quality events classification , 2010 .

[12]  N. D. Hatziargyriou,et al.  On-Line Preventive Dynamic Security of Isolated Power Systems Using Decision Trees , 2002, IEEE Power Engineering Review.

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

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

[15]  N. Abe,et al.  On the application of a machine learning technique to fault diagnosis of power distribution lines , 1995 .

[16]  Louis Wehenkel,et al.  Decision tree based transient stability method a case study , 1994 .

[17]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[18]  Emanuel Bernabeu,et al.  Methodology for a Security/Dependability Adaptive Protection Scheme Based on Data Mining , 2012, IEEE Transactions on Power Delivery.

[19]  Ruisheng Diao,et al.  Computation of transient stability related security regions and generation rescheduling based on decision trees , 2010, IEEE PES General Meeting.

[20]  S. Henry,et al.  Efficient Database Generation for Decision Tree Based Power System Security Assessment , 2011, IEEE Transactions on Power Systems.

[21]  Zhe Chen,et al.  A Systematic Approach for Dynamic Security Assessment and the Corresponding Preventive Control Scheme Based on Decision Trees , 2014, IEEE Transactions on Power Systems.

[22]  N.D. Hatziargyriou,et al.  Decision trees for dynamic security assessment and load shedding scheme , 2006, 2006 IEEE Power Engineering Society General Meeting.

[23]  Z. Vale,et al.  An electric energy consumer characterization framework based on data mining techniques , 2005, IEEE Transactions on Power Systems.

[24]  Enrique Lobato Miguélez,et al.  Decision trees applied to the management of voltage constraints in the Spanish market , 2005 .

[25]  Geza Joos,et al.  Catastrophe Predictors From Ensemble Decision-Tree Learning of Wide-Area Severity Indices , 2010, IEEE Transactions on Smart Grid.

[26]  S.M. Rovnyak,et al.  Response-based decision trees to trigger one-shot stabilizing control , 2004, IEEE Transactions on Power Systems.

[27]  S.M. Rovnyak,et al.  Integral square generator angle index for stability ranking and control , 2005, IEEE Transactions on Power Systems.

[28]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[29]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[30]  Nikos D. Hatziargyriou,et al.  A decision tree method for on-line steady state security assessment , 1994 .

[31]  I Kamwa,et al.  Development of rule-based classifiers for rapid stability assessment of wide-area post disturbance records , 2009, IEEE PES General Meeting.

[32]  Louis Wehenkel,et al.  Decision trees and transient stability of electric power systems , 1991, Autom..

[33]  Stavros A. Papathanassiou,et al.  Decision trees for fast security assessment of autonomous power systems with a large penetration from renewables , 1995 .

[34]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[35]  N. Senroy,et al.  Decision Tree Assisted Controlled Islanding , 2006, IEEE Transactions on Power Systems.

[36]  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.

[37]  Tingyan Guo,et al.  On-line prediction of transient stability using decision tree method — Sensitivity of accuracy of prediction to different uncertainties , 2013, 2013 IEEE Grenoble Conference.

[38]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[39]  L. Wehenkel,et al.  An Artificial Intelligence Framework for On-Line Transient Stability Assessment of Power Systems , 1989, IEEE Power Engineering Review.

[40]  Subhransu Ranjan Samantaray,et al.  Ensemble decision trees for phasor measurement unit-based wide-area security assessment in the operations time frame , 2010 .

[41]  H. Khoshkhoo,et al.  On-line dynamic voltage instability prediction based on decision tree supported by a wide-area measurement system , 2012 .

[42]  Louis Wehenkel,et al.  Automatic Learning Approaches for On-Line Transient Stability Preventive Control of the Hydro-Quebec System: Part II. A toolbox combining decision trees with neural nets and nearest neighbor classifiers otpimized by genetic algorithms , 1995 .

[43]  Chien-Chun Yang,et al.  Estimation of line flows and bus voltages using decision trees , 1994 .

[44]  Zhe Chen,et al.  Importance sampling based decision trees for security assessment and the corresponding preventive control schemes: The Danish case study , 2013, 2013 IEEE Grenoble Conference.

[45]  James S. Thorp,et al.  Decision trees for real-time transient stability prediction , 1994 .

[46]  Hong-Tzer Yang,et al.  Power system distributed on-line fault section estimation using decision tree based neural nets approach , 1995 .

[47]  Vijay Vittal,et al.  On-line Dynamic Security Assessment with Missing PMU Measurements : A Data Mining Approach , 2013 .