Research on Transient Stability Critical Clearing Time Calculation Based on Predictor-Corrector Cantor-Like Search

Abstract:

This paper proposes a new approach which applies Cantor-like search strategy to computing critical clearing time (CCT) after searching the initial search interval of fault clearing time (FCT) by advance-and-retreat predictor-corrector technology. On the basis of discussing the mathematical nature of computing CCT based on time-domain simulation, the work started with the comparison of conventional computer linear optimization search strategies, then combined Cantor-like search with transient stability analysis feature and realized CCT rapid calculation with the classical Dichotomy substituted. Simulations on New England 10-generator 39-bus system demonstrate that this new algorithm has a good advantage over efficiency and precision, as well as its engineering application prospect.

• Periodical: Applied Mechanics and Materials (Volumes 448-453)
• Pages: 2573-2577
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.448-453.2573
• Online since: October 2013
• Authors: Long Xiang Huang*, Feng Zhao, Wei Jiang Qiu, Shi Hong Miao
• Keywords: Cantor-Like Method, Critical Clearing Time (CCT), Linear Optimization Search, Power System Transient Stability

References

[1] T. Miki, D. Okitsu, E. Takashima, Power system transient stability assessment using critical fault clearing time functions, IEEE/PES Asia-Pacific Transmission and Distribution Conference and Exhibition, (2002), pp.1514-1517.

DOI: 10.1109/tdc.2002.1176822

[2] Yiqiao Liang, S. Ayasun, C. Nwankpa, Calculation of the probability density function of critical clearing time in transient stability analysis, Proceedings of the 35th Annual Hawaii International Conference on System Sciences, (2002), pp.1-8.

DOI: 10.1109/hicss.2002.993972

[3] P.K. Olulope, K.A. Folly, S.P. Chowdhury, Prediction of critical clearing time using artificial neural network, IEEE Symposium on Computational Intelligence Applications in Smart Grid, (2011), pp.1-5.

DOI: 10.1109/ciasg.2011.5953345

[4] Hung Nguyen Dinh, Yong Tae Yoon, Two novel methods for real time determining critical clearing time SIME-B and CCS-B based on clustering identification, 3rd International Conference on Intelligent Systems, Modelling and Simulation, (2012).

DOI: 10.1109/isms.2012.106

[5] G.A. Maria, C. Tang, J. Kim: IEEE Trans. on Power Systems, Vol. 5 (1990), pp.384-393.

[6] Yude Yang, Hua Wei, Xiaohui Zhao, Direct critical clearing time calculation based on interior point nonlinear programming, IEEE/PES Asia-Pacific Power and Energy Engineering Conference, (2012), pp.1-6.

DOI: 10.1109/appeec.2012.6307467

[7] N. Yorino, A. Priyadi, H. Kakui: IEEE Trans. on Power Systems, Vol. 25 (2010) pp.1620-1626.

[8] Dazhong Fang, Yao Zhang, Wennan Song: Proceedings of the CSEE, In Chinese, Vol. 18 (1998), pp.200-205.

[9] P. Kundur, N.J. Balu, M.G. Lauby: Power System Stability and Control (McGraw-Hill Edu. (India) Pvt Ltd, New York, 1994).

[10] A.T. Hamdy: Operations Research: An Introduction (Pearson Edu. Ltd, New Jersey, 2011).

[11] Jiongcheng Li, Henghui Xiao, Guiyu Li: Computer Engineering and Applications, In Chinese, Vol. 49 (2013), pp.53-56.

[12] Y. S. Zhong: IEE Proceedings: Control Theory and Applications, (2001), pp.70-80.