Artificial neural network based early detection of real-time transient instability for initiation of emergency control through wide-area synchrophasor measurements

This paper proposes an approach for early detection of transient instability of power system for initiating the emergency control in time. The synchrophasor measurements are used for real-time monitoring of the system. The Artificial Neural Network (ANN) is used as classifier for predicting the transient instability status of the system with rotor angles and speeds (frequency) of generator as inputs at different consecutive cycle lengths after fault clearing. The stability status obtained from ANN can be utilized for initiating the emergency control actions within few cycles from fault clearing. The proposed scheme is able to successfully predict the transient stability status of the system for unseen operating conditions with varying topology. The proposed method is investigated on IEEE-39 New England system for its real-time applications and results obtained reflect the effectiveness of the proposed methodology.

[1]  M.A. El-Sharkawi,et al.  Support vector machines for transient stability analysis of large-scale power systems , 2004, IEEE Transactions on Power Systems.

[2]  Carson W. Taylor,et al.  Definition and Classification of Power System Stability , 2004 .

[3]  S. Lindahl,et al.  Wide area protection and emergency control , 2004 .

[4]  Peter Crossley,et al.  Rotor angle instability prediction using post-disturbance voltage trajectories , 2010, IEEE PES General Meeting.

[5]  A.G. Phadke,et al.  Wide Area Protection—Technology and Infrastructures , 2006, IEEE Transactions on Power Delivery.

[6]  M. Pavella,et al.  A comprehensive approach to transient stability control part II: open loop emergency control , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[7]  Sarosh N. Talukdar,et al.  Iterative Multistep Methods for Transient Stability Studies , 1971 .

[8]  Vijay Vittal,et al.  Critical Energy for Direct Transient Stability Assessment of a Multimachine Power System , 1984, IEEE Power Engineering Review.

[9]  Mania Pavella,et al.  A comprehensive approach to transient stability control. II. Open loop emergency control , 2003 .

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

[11]  F. Milano,et al.  An open source power system analysis toolbox , 2005, 2006 IEEE Power Engineering Society General Meeting.

[12]  Chih-Wen Liu,et al.  New methods for computing power system dynamic response for real-time transient stability prediction , 2000 .

[13]  M. Ribbens-Pavella,et al.  Extended Equal Area Criterion Justifications, Generalizations, Applications , 1989, IEEE Power Engineering Review.

[14]  C. Jensen,et al.  Power System Security Assessment Using Neural Networks: Feature Selection Using Fisher Discrimination , 2001, IEEE Power Engineering Review.

[15]  Mu-Chun Su,et al.  Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements , 1999 .

[16]  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.

[17]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[18]  N. Amjady,et al.  Transient Stability Prediction by a Hybrid Intelligent System , 2007, IEEE Transactions on Power Systems.

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[21]  Harinder Sawhney,et al.  A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment , 2006 .

[22]  Vijay Vittal,et al.  Critical Energy for Direct Transient Stability Assessment of a Multimachine Power System , 1984, IEEE Transactions on Power Apparatus and Systems.