On-line determination of transient stability status using multilayer perceptron neural network

Frimpong, Emmanuel Asuming; Okyere, Philip Yaw; Asumadu, Johnson

doi:10.1515/jee-2018-0008

Abstract

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

References

[1] T. Rahman, S. Sankaran, N. Seeley and K. Garg, “Capturing Generator Rotor Angle and Field Quantities SDG&E Experience and Approach to Using Nontraditional Generator Measurements”, Proceedings of 42-nd Annual Western Protective Relay Conference, Washington, 2015.10.1109/CPRE.2016.7914880
Search in Google Scholar Back to article
[2] A. D. Rajapakse, F. Gomez, O. M. K. K. Nanayakkara, P. A. Crossley, and V. V. Terzija “Rotor Angle Stability Prediction Using Post-disturbance voltage trajectory patterns”, Power & Energy Society General meeting, pp. 1-6, 2009.10.1109/PES.2009.5275270
Search in Google Scholar Back to article
[3] P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. V. Cutsem and V. Vittal, “Definition and Classification of Power System Stability”, IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 1387-1401, 2004.
Search in Google Scholar Back to article
[4] N. Amjady and S. F. Majedi, “Transient stability prediction by a hybrid intelligent system”, IEEE Transaction on Power Systems, vol. 22, no.3, pp. 1275 - 1283, 2007.10.1109/TPWRS.2007.901667
Open DOI Search in Google Scholar Back to article
[5] D. R. Gurusinghe and A. D. Rajapakse, “Post-Disturbance Transient Stability Status Prediction Using Synchrophasor Measurements”, IEEE Transactions on Power Systems, vol. 31, no.5, pp. 3656-3664, 2016.
Search in Google Scholar Back to article
[6] A. N. AL-Masri, M. Z. A. A. Kadir, H. Hizam and N. Mariun, “A Novel Implementation for Generator Rotor Angle Stability Prediction Using an Adaptive Artificial Neural Network Application for Dynamic Security Assessment”, IEEE Transactions on Power Systems, vol. 28, no.3, pp. 2516-2525, 2013.
Search in Google Scholar Back to article
[7] Y. Zhou, J. Wu, Z. Yu, L. Ji and L. Hao, “A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier”, Energies, vol. 9, pp. 1-20, 2016.10.3390/en9100778
Search in Google Scholar Back to article
[8] J. Hazra, R. K. Reddi, K. Das, D. P. Seetharam and A. K. Sinha, “Power Grid Transient Stability Prediction Using Wide Area Synchrophasor Measurements”, Proceeding of 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, pp. 1-8, 2012.10.1109/ISGTEurope.2012.6465752
Search in Google Scholar Back to article
[9] T. Guo and J. V. Milanović, “On-line Prediction of Transient Stability Using Decision Tree Method - Sensitivity of Accuracy of Prediction to Different Uncertainties”, Proceedings of the IEEE Grenoble PowerTech, Grenoble, 2013.10.1109/PTC.2013.6652106
Search in Google Scholar Back to article
[10] F. Gomez, A. U. Rajapakse and I. Annakkage and Fernando, “Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements”, IEEE Transactions on Power Systems, vol. 26, no.3, pp. 1474-1483, 2011.
Search in Google Scholar Back to article
[11] J. Q. Zhao, J. Li, X. C. Wu, K. Men, C. Hong and Y. J. Liu, “A novel real-time transient stability prediction method based on post-disturbance voltage trajectories”, Proceedings of the International Conference on Advanced Power System Automation and Protection, Beijing, pp. 730-736, 2011.10.1109/APAP.2011.6180495
Search in Google Scholar Back to article
[12] A. D. Rajapakse, F. Gomez, O. M. K. K. Nanayakkara, P. A. Crossley and V. V. Terzija, “Rotor angle stability prediction using post-disturbance voltage trajectory patterns”, IEEE Transactions on Power Systems, vol. 25, no.2, pp. 945-956, 2010.10.1109/TPWRS.2009.2036265
Search in Google Scholar Back to article
[13] D. E. Echeverra, J. L. Rueda, J. C. Cepeda1, D. G. Colomé and I. Erlich, “Comprehensive approach for prediction and assessment of power system transient stability real-time”, Proceedings of the 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Copenhagen, pp. 1-5, 2013.10.1109/ISGTEurope.2013.6695394
Search in Google Scholar Back to article
[14] A. Y. Abdelaziz and M. A. El-Dessouki, “Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques”, I. J. Intelligent Systems and Applications, vol. 10, pp. 1-10, 2013.10.5815/ijisa.2013.10.01
Search in Google Scholar Back to article
[15] IEEE Standard for Synchrophasor Measurements for Power Systems, IEEE Std.C37.118.1-2011, December 2011.
Search in Google Scholar Back to article
[16] Power System Simulator for Engineers PSSE University Edition, 2016.
Search in Google Scholar Back to article
[17] M. H. Beale, M. T. Hagan, H. B. Demuth, Neural Network ToolboxTM, User Guide, MATLAB, R2016b, pp. 3-2 - 3-5, 2016.
Search in Google Scholar Back to article
[18] C. K. Özkan and F. S. Erbek, “The Comparison of Activation Functions for Multispectral Landsat TM Image Classification”, Photogrammetric Engineering & Remote Sensing, vol. 69, no.11, pp. 1225-1234, 2013.
Search in Google Scholar Back to article
[19] B. Naduvathuparambil, M. C. Valenti and A. Feliachi, “Communication Delays Wide Area Measurement Systems”, Proceedings of the thirty-fourth Southeastern Symposium on System Theory, Huntsville, 2002.
Search in Google Scholar Back to article

On-line determination of transient stability status using multilayer perceptron neural network

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