Artificial neural network based algorithm for early prediction of transient stability using wide area measurements

Early prediction of the transient stability of power systems after fault occurrences has a great impact on the performance of wide area protection and control systems designed against transient instabilities. In this paper, an artificial neural networks based methodology is proposed for predicting the power system stability directly after clearing the fault. A dataset is generated to train a multilayer perceptron off-line, which is then used for early online prediction of any transient instability. The neural network is fed by the inputs, which are the pre-fault, during-fault, and post-fault voltage magnitude measurements collected from the phasor measurement units. The success and the effectiveness of the proposed method are demonstrated, as it is applied to the 37-generator 127-bus power test system and an accuracy above 99% is obtained in the early prediction of transient instabilities.

[1]  Stephen V. Stehman,et al.  Selecting and interpreting measures of thematic classification accuracy , 1997 .

[2]  Vahid Madani,et al.  Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks , 2011, Proceedings of the IEEE.

[3]  Peter A Crossley,et al.  Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories , 2010, IEEE Transactions on Power Systems.

[4]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[5]  Bo Wang,et al.  Power System Transient Stability Assessment Based on Big Data and the Core Vector Machine , 2016, IEEE Transactions on Smart Grid.

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

[7]  H. Chiang.  Direct Methods for Stability Analysis of Electric Power Systems: Theoretical Foundation, BCU Methodologies, and Applications , 2010 .

[8]  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.

[9]  Dawei Fan,et al.  Synchronized Measurements And Applications During Power System Dynamics , 2008 .

[10]  U. D. Annakkage,et al.  Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements , 2011, IEEE Transactions on Power Systems.

[11]  Rui Zhang,et al.  Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Jovica V. Milanovic,et al.  Probabilistic Framework for Assessing the Accuracy of Data Mining Tool for Online Prediction of Transient Stability , 2014, IEEE Transactions on Power Systems.

[13]  Dinesh Rangana Gurusinghe,et al.  Post-Disturbance Transient Stability Status Prediction Using Synchrophasor Measurements , 2016, IEEE Transactions on Power Systems.

[14]  Norman Mariun,et al.  A Novel Implementation for Generator Rotor Angle Stability Prediction Using an Adaptive Artificial Neural Network Application for Dynamic Security Assessment , 2013, IEEE Transactions on Power Systems.

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

[16]  K. R. Padiyar,et al.  ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY , 1990 .

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

[18]  Athula D. Rajapakse,et al.  Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network , 2010, IEEE Transactions on Power Systems.

[19]  Savu Crivat Savulescu.  Real-Time Stability Assessment in Modern Power System Control Centers , 2009 .