پیش‌بینی هوشمند ناپایداری گذرا پس از وقوع اغتشاش با استفاده از سیستم اندازه‌گیری ناحیه گسترده

نویسندگان

دانشکده مهندسی برق و کامپیوتر - دانشگاه صنعتی جندی شاپور

چکیده

در این مقاله حل مسئله پیش‌بینی ناپایداری گذرا پس از یک اغتشاش با استفاده از سیستم اندازه‌گیری ناحیه گسترده مدنظر است. در طرح ارائه‌شده، ابتدا با استفاده از اندازه فازورهای ولتاژ دریافتی از سوی واحدهای اندازه‌گیری فازور، لحظه وقوع اغتشاش تشخیص داده می‌شود. در مرحله دوم، مبتنی بر داده‌های ارسال‌شده پس از وقوع اغتشاش شامل اندازه فازور ولتاژ، فرکانس و مشتق فرکانس، تصمیم اولیه در خصوص وضعیت پایداری گذرا توسط سه طبقه‌بندی‌کننده از قبل آموزش داده‌شده ماشین بردار پشتیبان به‌صورت مجزا صورت می‌پذیرد. در پایان، سه تصمیم اولیه با استفاده از روش بیز، ترکیب و تصمیم نهایی در خصوص وضعیت پایداری گذرا اعلام می‌گردد. طرح مذکور بر روی شبکه استاندارد 39 باسه IEEE پیاده‌سازی شده است. نتایج نشان می‌دهند الگوریتم پیشنهادی قادر به پیش‌بینی ناپایداری گذرا در مدت زمان کمتر از سه سیکل پس از وقوع اغتشاش و با دقت قابل‌قبول در شرایط حضور و عدم حضور نویز در داده‌های واحدهای اندازه‌گیری فازور است.

کلیدواژه‌ها


عنوان مقاله [English]

Intelligent Post-Disturbance Transient Instability Prediction using Wide Area Measurement System

نویسندگان [English]

  • S. Afshari
  • M. Sarlak
Faculty of Electrical and Computer Engineering, Jundi Shapur University of Technology, Dezful, Iran
چکیده [English]

In this paper, the solution of the post-disturbance transient instability prediction problem using the wide area system is considered. In the proposed scheme, at first, the disturbance initiation is detected based on the magnitudes of voltage phasors received from phasor measurement units (PMUs). In the second step, a primary assessment of the transient stability is made according to the post-disturbance magnitudes of voltage phasors, frequency, and differential frequency and by three trained support vector machines (SVMs) classifiers, separately. Finally, the outputs of the classifiers are combined employing the Naive Bayes (NB) algorithm to make the final decision. The proposed algorithm was implemented on the IEEE New England 39-bus system. According to the results obtained, proposed algorithm can predict the transient instability as early as three cycles after the disturbance initiation using noise-free and noisy PMU data.

کلیدواژه‌ها [English]

  • Phasor measurement units
  • wide area measurement system
  • transient instability
  • Support Vector Machine
  • combination of classifiers
  • bayes method
[1] F. Hashiesh, H. E. Mostafa, A.-R. Khatib, I. Helal and M. M. Mansour, “An intelligent wide area synchrophasor based system for predicting and mitigating transient instabilities,” IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 645-652, 2012.
[2] T. Guo and J. V. Milanovic, “Probabilistic framework for assessing the accuracy of data mining tool for online prediction of transient stability,” IEEE Transactions on Power Systems, vol. 29, no. 1, pp. 377-385, 2014.
[3] J. Hazra, R. K. Reddi, K. Das, D. P. Seetharam and A. K. Sinha, “Power grid transient stability prediction using wide area synchrophasor measurements,” IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, pp. 1-8, 2012.
[4] R. Zhang, Y. Xu, Z. Y. Dong and K. P. Wong, “Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system,” IET Generation, Transmission & Distribution, vol. 9, no. 3, pp. 296-305, 2015.
[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.
[6] A. Pai, Energy function analysis for power system stability, Kluwer Academic Publishers, 1989.
[7] M. Pavella, D. Ernst and D. Ruiz-Vega, Transient stability of power systems: a unified approach to assessment and control, Kluwer Academic Publishers, 2000.
[8] P. Kundur, N. J. Balu and M. G. Lauby, Power system stability and control, New York: McGraw-Hill, 1994.
[9] L. Wehenkel, T. Van Cutsem and M. Ribbens-Pavella, “An artificial intelligence framework for online transient stability assessment of power systems,” IEEE Transactions on Power Systems, vol. 4, no. 2, pp. 789-800, 1989.
[10] F. Hashiesh, H. E. Mostafa, I. Helal and M. M. Mansour, “A wide area synchrophasor based ANN transient stability predictor for the Egyptian Power System,” Innovative Smart Grid Technologies Conference, pp. 1-7, 2010.
[11] سهیل مرادی، رضا محمدی چبنلو، نوید تقیزادگان کلانتری، «ارزیابی برون خط پایداری گذرا به وسیله تعیین دقیق CCT با استفاده از شبکه عصبی با ورودی‌های مبتنی بر توابع انرژی»، مجله مهندسی برق دانشگاه تبریز، دوره 46 ،شماره 1 ،صفحه285-277، 1395.
[12] A. Gavoyiannis, D. Vogiatzis, D. Georgiadis and N. Hatziargyriou, “Combined support vector classifiers using fuzzy clustering for dynamic security assessment,” Power Engineering Society Summer Meeting, vol. 2, pp. 1281-1286, 2001.
[13] T. Amraee and S. Ranjbar, “Transient instability prediction using decision tree technique,” IEEE Transactions on power systems, vol. 28, no. 3, pp. 3028-3037, 2013.
[14] L. Wehenkel, M. Pavella, E. Euxibie and B. Heilbronn, “Decision tree based transient stability method a case study,” IEEE Transactions on Power Systems, vol. 9, no. 1, pp. 459-469, 1994.
[15] L. S. Moulin, A. A. Da Silva, M. El-Sharkawi and R. J. Marks, “Support vector machines for transient stability analysis of large-scale power systems,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 818-825, 2004.
[16] F. R. Gomez, A. D. Rajapakse, U. D. Annakkage and I. T. 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.
[17] I. Kamwa, R. Grondin and L. Loud, “Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques,” IEEE Transactions on Power Systems, vol. 16, no. 3, pp. 526-536, 2001.
[18] I. Kamwa, S. Samantaray and G. Joos, “Development of rule-based classifiers for rapid stability assessment of wide-area post-disturbance records,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 258-270, 2009.
[19] J. C. Cepeda, J. L. Rueda, D. G. Colomé and D. E. Echeverría, “Real-time transient stability assessment based on centre-of-inertia estimation from phasor measurement unit records,” IET Generation, Transmission & Distribution, vol. 8, no. 8, pp. 1363-1376, 2014.
[20] I. Kamwa, S. Samantaray and G. Joos, “Catastrophe predictors from ensemble decision-tree learning of wide-area severity indices,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 144-158, 2010.
[21] M. Li, A. Pal, A. G. Phadke and J. S. Thorp, “Transient stability prediction based on apparent impedance trajectory recorded by PMUs,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 498-504, 2014.
[22] S. Rovnyak, S. Kretsinger, J. Thorp and D. Brown, “Decision trees for real-time transient stability prediction,” IEEE Transactions on Power Systems, vol. 9, no. 3, pp. 1417-1426, 1994.
[23] A. D. Rajapakse, F. Gomez, K. Nanayakkara, P. A. Crossley and V. V. Terzija, “Rotor angle instability prediction using post-disturbance voltage trajectories,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 947-956, 2010.
[24] N. Amjady and S. F. Majedi, “Transient stability prediction by a hybrid intelligent system,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1275-1283, 2007.
[25] N. Amjady and S. Banihashemi, “Transient stability prediction of power systems by a new synchronism status index and hybrid classifier,” IET generation, transmission & distribution, vol. 4, no. 4, pp. 509-518, 2010.
[26] L. I. Kuncheva, Combining pattern classifiers: methods and algorithms, John Wiley & Sons, 2004.
[27] M. Brown, M. Biswal, S. Brahma, S. J. Ranade and H. Cao, “Characterizing and quantifying noise in PMU data,” Power and Energy Society General Meeting, pp. 1-5, 2016.
[28] K. E. Martin, “Synchrophasor standards development-IEEE C37. 118 & IEC 61850,” 44th Hawaii International Conference on System Sciences, pp. 1-8, 2011.
[29] S. Tulyakov, S. Jaeger, V. Govindaraju and D. Doermann, “Review of classifier combination methods," Machine Learning in Document[1] Analysis and Recognition, pp. 361-386, 2008.
[30] S. Li, J. Liu, Y. Zhu and X. Zhang, “A new supervised clustering algorithm for data set with mixed attributes,” International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 2, pp. 844-849, 2007.
[31] سهیل مرادی، رضا محمدی چبنلو، نوید تقیزادگان کلانتری، «مکانیابی بهینه واحدهای اندازه‌گیر فازوری برای مکانیابی خطا در شبکه قدرت با در نظر گرفتن باسهای تزریق صفر و خروج تکی خطوط»، مجله مهندسی برق دانشگاه تبریز، دوره 46 ،شماره 2 ،صفحه277-267، 1395.
[32]      I. Steinwart and A. Christmann, Support vector machines, Springer, 2008.
[33]      C. L. Hoang and C. J. Wang, “A GA-based feature selection and parameters optimization for support vectormachines,” Expert Syst. Apl., vol. 31, pp. 231–240, 2006.
[34]      F. Gomez, “Prediction and control of transient instability using wide area phasor measurements,” PhD thesis, University of Manitoba, 2011.
[35]      B. Naduvathuparambil, M. C. Valenti and A. Feliachi, “Communication delays in wide area measurement systems”, Proceedings of the Thirty-Fourth Southeastern Symposium on System Theory, pp. 118-122, 2002.
[36]      J. J. Qiao Yu, D. J. Hill, A. S. Lam, J. Gu and V. O. K. Li, “Intelligent Time-Adaptive Transient Stability Assessment System”, IEEE Transactions on Power Systems, To be published, 2017.
[37]      J. C. Cepeda, J. L. Rueda, D.G. Colomé and I. Erlich, “Data-mining-based approach for predicting the power system post-contingency dynamic vulnerability status”, Int. Trans. Electrical Energy Systems, vol. 25, no. 10, pp. 2515-2546, 2014.