Blackout Estimation by Neural Network

Blackout Estimation by Neural Network

Автор: Mohammad Reza Salimian, Mohammad Reza Aghamohammadi

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 7 vol.8, 2016 года.

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Cascading failures play an important role in creation of blackout. These events consist of lines and generators outages. Online values of voltage, current, angle, and frequency are changing during the cascading events. The percent of blackout can be estimated during the disturbance by neural network. Proper indices must be defined for this purpose. These indices can be computed by online measurement from WAMs. In this paper, voltage, load, lines, and generators indices are defined for estimating the percent of blackout during the disturbance. These indices are used as the inputs of neural networks. A new combinational structure of neural network is used for this purpose. Proposed method is implemented on 39-bus New-England test system.

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Cascading failures, Neural network, Blackout, stability, Distance relay

Короткий адрес: https://sciup.org/15010838

IDR: 15010838

Список литературы Blackout Estimation by Neural Network

  • Samantaray, S. R. "Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line." IET generation, transmission & distribution 3.5 (2009): 425-436.
  • Ozgonenel, Okan, and SerapKaragol. "Power transformer protection based on decision tree approach." Electric Power Applications, IET 8.7 (2014): 251-256.
  • Liu, Chengxi, et al. "A systematic approach for dynamic security assessment and the corresponding preventive control scheme based on decision trees." IEEE Trans. Power Syst., vol. 29, no. 2, pp. 717-730, Feb. 2014.
  • G. V. Kass, “An exploratory technique for investigating large quantities of categorical data,” Appl. Statist., vol. 29, no. 2, pp. 119–127, 1980.
  • L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth International Group, 1984.
  • Mohammad Reza Salimian, "Transient Stability Prediction by ANFIS." International Electrical Engineering Journal, Vol. 5 (2014) No.3, pp. 1287-1292 .
  • C. W. Liu, M. C. Su, S. S. Tsay, and Y. J. Wang, “Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements,” IEEE Trans. Power Syst., vol. 14, no. 2, pp. 685–692, May 1999.
  • G. Bahbah and A. Girgis “New Method for Generators’ Angles and Angular Velocities Prediction for Transient Stability Assessment of Multi machine Power Systems Using Recurrent Artificial Neural Network,” IEEE Trans. Power Syst., vol. 19, pp. 1015–1022, MAY. 2004.
  • N. Amjady and S. F. Majedi, “Transient stability prediction by a hybrid intelligent system,” IEEE Trans. Power Syst., vol. 22, pp. 1275-1283, Aug. 2007.
  • M. Mohammadi, and H. Ebrahimpour-Komleh. "Rule Based Ensembles Using Pair Wise Neural Network Classifiers." International Journal of Intelligent Systems and Applications (IJISA), vol. 7, no. 4, 2015.
  • R. K. Sharma, and A. K. Gupta. "Voice Analysis for Tele diagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines." International Journal of Intelligent Systems and Applications (IJISA), vol. 7, no. 6, 2015.
  • N. A. Kumar, and A. P. Singh. "Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks." International Journal of Intelligent Systems and Applications (IJISA), vol. 7, no. 7, 2015.
  • K., Yabe, et al. "Conceptual designs of AI-based systems for local prediction of voltage collapse." IEEE Trans. Power Syst., vol. 11, no 1, pp. 137-145, Nov. 1996.
  • S. Tamronglak. S. H. Horowitz, A.G. Phadke, and J.S. Thorp, "Anatomy of power system blackouts: Preventive relaying strategies,” IEEE Trans. Power Delivery, vol. 11, no. 2, pp. 708–715, Apr. 1996.
  • J, M, Hendrickx., et al. "Efficient computations of a security index for false data attacks in power networks." IEEE Trans. Automatic Contro, vol. 59, no. 12, pp. 3194-3208, Dec. 2014.
  • G. J. Lee, M. M. Albu, and G. T. Heydt, “A power quality index based on equipment sensitivity, cost and network vulnerability,” IEEE Trans. Power Del., vol. 19, no. 3, pp. 1504–1510, Jul. 2004.
  • A, Dwivedi, and Y, Xinghuo "A maximum-flow-based complex network approach for power system vulnerability analysis." IEEE Trans. Industrial Informatics., vol. 9, no. 1, pp. 81-88, Feb. 2013.
  • I. Kamwa, et al. “Fuzzy Partitioning of a Real Power System for Dynamic Vulnerability Assessment,” IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1356-1365, Aug. 2009.
  • S. Mei and Y. Ni, “A Study of Self-Organized Criticality of Power System Under Cascading Failures Based on AC-OPF With Voltage Stability Margin,” IEEE Trans.PowerSyst.,vol. 23, no. 4, pp. 1719–1728, Nov. 2008.
  • G. j. Correa, “Grid vulnerability analysis based on scale-free graphs versus power flow models,” Elect. Power Systems Research., vol. 11, pp. 71–79, Apr. 2013.
  • M. Song, “A new Analysis method for early detection and prevention of cascadingevents,” Elect. Power Systems Research., vol. 77, pp. 1132–1142, Nov. 2007.
  • M. A. Haidar, “Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods,” Journal of Electrical Engineering & Technology., vol. 3, no. 2, pp. 167-176, 2008.
  • D. Ruisheng, et al. "Decision tree assisted controlled islanding for preventing cascading events." IEEE/PES Power Systems Conference and Exposition, 2009.
  • H. Manjari, and B. H. Chowdhury, "Intentional islanding and adaptive load shedding to avoid cascading outages." IEEE Power Engineering Society General Meeting, 2006.
  • Koch, Stephan, et al. "Mitigation of cascading failures by real-time controlled islanding and graceful load shedding." IEEE Bulk Power System Dynamics and Control (iREP), 2010.
  • A. S. Shahnawaz, et al. "A scheme for controlled islanding to prevent subsequent blackout." IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 136-143, 2003.
  • K. Sun, K. Hur, P. Zhang, “A New Unified Scheme for Controlled Power System Separation Using Synchronized Phasor Measurements, ” IEEE Trans. Power Syst., vol. 26, No. 3, pp. 1544-1554, Aug. 2011.
  • T. B. Nguyen and M. A. Pai, “Dynamic security constrained rescheduling of power systems using trajectory sensitivities,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 848–854, May 2003.
  • M.R. Aghamohammadi, S. hashemi and A. hasanzade, ‘‘A new approach for mitigating blackout risk by blocking minimum critical distance relays,’’ Int. J. Elect. Power Energy Syst., vol. 75, pp. 162-172, 2016.
  • M.R. Aghamohammadi, M.A. Hasanzade, “A New Approach for Modeling Self Triggering Characteristic of Cascading Failure in Power System Blackout,” 17rd ICEE,2009.
  • M. Zare, MR. Aghamohammadi, and M. Saeedi, “Mitigation of power system blackout by blocking zone 3 of minimum distance relays,’’ In: Universities Power Engineering Conference (UPEC), Italy; 2008. p. 1–5.
  • SPSS Modeler Software User’s Guide, Version 14.2, 2010.
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