Wavelet Analysis and Radial Basis Function Neural Network Based Stability Status Prediction Scheme
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Keywords
Abstract
This paper presents a technique for predicting the transient stability status of a power system. Bus voltages of system generators are used as input parameter. The bus voltages are processed using wavelet transform. Daubechies 8 mother wavelet is employed to extract wavelet entropy of detail 1 coefficients. The sum of wavelet entropies is used as input to a trained radial basis function neural network which predicts the transient stability status. The IEEE 39-bus test system was used to validate the effectiveness and applicability of the technique. The technique is simple to apply and can be implemented in real-time. The prediction accuracy was found to be 86.5% for 200 test cases.
Keywords : Radial basis function, Transient analysis and Wavelet transform
References
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[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.
[7] 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.
[8] 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.
[9] S. Pittner and S. V. Kamarthi, “Feature Extraction from Wavelet Coefficients for Pattern Recognition Tasks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 1, pp. 83-88, 1990.
[10] D. Chanda, N. K. Kishore and A. K. Sinha “Application of Wavelet Multiresolution Analysis for Classification of Faults on Transmission lines”, in IEEE Conference on Convergent Technologies for the Asia-Pacific Region, pp. 1464-1469, 2003.
[11] M. Uyar, S. Yildirim and M. T. Gencoglu, “An Effective Wavelet-Based Feature Extraction Method for Classification of Power Quality Disturbance Signals”, Electric Power Systems Research, vol. 78, pp. 1747–1755, 2008.
[12] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann and E. Başar, “Wavelet entropy: a new tool for analysis of short duration brain electrical signals”, Journal of Neuroscience Methods, vol. 105, pp. 65-75, 2001.
[13] Z. He, S. Gao, X. Chen, J. Zhang, Z. Bo and Q. Qian, “Study of a new method for power system transients classification based on wavelet entropy and neural network”, Electrical Power and Energy Systems, vol. 33, pp. 402-410, 2011.
[14] A. Karami, “Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation”, Journal of Electrical Engineering & Technology, vol. 3, no. 4, pp. 468-475, 2008.
[15] M. H. Beale, M. T. Hagan and H. B. Demuth, Neural Network ToolboxTM, User Guide, MATLAB, R2016b, 2016.
[16] D. Hou, “Relay Element Performance During Power, System Frequency Excursions”, in 61st Annual Conference for Protective Relay Engineers College Station. 2008.
[17] Y. Song, “Design of Secondary Voltage and Stability Controls with Multiple Control Objectives”, PhD, School of Electrical and Computer Engineering, Georgia Institute of Technology, Georgia, 2009.
[2] Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations, (April, 2004) Available: http://www.nerc.com/filez/blackout.html
[3] 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.
[4] H.-Z. Guo, H. Xie, B.-H. Zhang, G.-L. Yu, P. Li, Z.-Q. Bo, and A. Klimek, “Study on Power System Transient Instability Detection Based on Wide Area Measurement System”, European Transactions on Electrical Power, vol. 20, pp. 184–205, 2010.
[5] E. A. Frimpong, P. Y. Okyere and J. A. Asumadu, “On-line determination of transient stability status using multilayer perceptron neural network”, Journal of Electrical Engineering, vol. 69, no. 1, pp. 1-7, 2018.
[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.
[7] 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.
[8] 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.
[9] S. Pittner and S. V. Kamarthi, “Feature Extraction from Wavelet Coefficients for Pattern Recognition Tasks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 1, pp. 83-88, 1990.
[10] D. Chanda, N. K. Kishore and A. K. Sinha “Application of Wavelet Multiresolution Analysis for Classification of Faults on Transmission lines”, in IEEE Conference on Convergent Technologies for the Asia-Pacific Region, pp. 1464-1469, 2003.
[11] M. Uyar, S. Yildirim and M. T. Gencoglu, “An Effective Wavelet-Based Feature Extraction Method for Classification of Power Quality Disturbance Signals”, Electric Power Systems Research, vol. 78, pp. 1747–1755, 2008.
[12] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann and E. Başar, “Wavelet entropy: a new tool for analysis of short duration brain electrical signals”, Journal of Neuroscience Methods, vol. 105, pp. 65-75, 2001.
[13] Z. He, S. Gao, X. Chen, J. Zhang, Z. Bo and Q. Qian, “Study of a new method for power system transients classification based on wavelet entropy and neural network”, Electrical Power and Energy Systems, vol. 33, pp. 402-410, 2011.
[14] A. Karami, “Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation”, Journal of Electrical Engineering & Technology, vol. 3, no. 4, pp. 468-475, 2008.
[15] M. H. Beale, M. T. Hagan and H. B. Demuth, Neural Network ToolboxTM, User Guide, MATLAB, R2016b, 2016.
[16] D. Hou, “Relay Element Performance During Power, System Frequency Excursions”, in 61st Annual Conference for Protective Relay Engineers College Station. 2008.
[17] Y. Song, “Design of Secondary Voltage and Stability Controls with Multiple Control Objectives”, PhD, School of Electrical and Computer Engineering, Georgia Institute of Technology, Georgia, 2009.
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Published
Oct 17, 2018
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How to Cite
Frimpong, E. A., Okyere, P. Y., & Asumadu, J. (2018). Wavelet Analysis and Radial Basis Function Neural Network Based Stability Status Prediction Scheme. Jurnal Nasional Teknik Elektro, 7(3), 146–152. https://doi.org/10.25077/jnte.v7n3.559.2018
Section
Electrical Power and Energy
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