Journal of Electrical Engineering and Technology

  • 제11권6호
  • /
  • Pages.1527-1534
  • /
  • 2016
  • /
  • 1975-0102(pISSN)
  • /
  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Extreme Learning Machine Approach for Real Time Voltage Stability Monitoring in a Smart Grid System using Synchronized Phasor Measurements

  • Duraipandy, P. (Dept. of Electrical and Electronics Engineering, Velammal College of Engineering & Technology) ;
  • Devaraj, D. (Dept. of Electrical and Electronics Engineering, Kalasalingam University)
  • 투고 : 2014.12.30
  • 심사 : 2016.07.09
  • 발행 : 2016.11.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

키워드

참고문헌

  1. IEEE Special Publication, 90TH0358-2-PWR, "Voltage Stability of Power Systems: Concepts, Analytical Tools and Industry Experience," 1990.
  2. Tiranuchit and R. J. Thomas, "A Posturing Strategy Against Voltage Instabilities in Electric Power Systems," IEEE Transactions on Power Systems, Vol. 3, No. 1, pp. 87-93, Feb. 1998.
  3. P.A. Lof, T Smed, G. Anderson and D. J. Hill, "Fast Calculation of a Voltage Stability Index," IEEE Transactions on Power Systems, Vol. 7, No. 1, pp. 54-64, Feb. 1992.
  4. P. Kessel and H. Glavitsch, "Estimating the Voltage Stability of Power Systems," IEEE Transactions on Power Systems, Vol.1, No.3, pp. 346-354, July 1986. https://doi.org/10.1109/TPWRD.1986.4308013
  5. Gao. G.K. Morison and P. Kundur, "Voltage Stability Evaluation using Modal Analysis," IEEE Transactions on Power Systems, Vol. 7, No. 4, pp. 1529-1542, Nov. 1992. https://doi.org/10.1109/59.207377
  6. P.A. Lof, G. Anderson and D.J. Jill, "Voltage Stability Indices for Stressed Power System," IEEE Transactions on Power Systems, Vol. 8, No. 1, pp. 326-335, Feb. 1993. https://doi.org/10.1109/59.221224
  7. C.A. Canizares, A.Z. de Souza and V.H. Quintana, "Comparison of Performance Indices for Detection of Proximity to Voltage Collapse," IEEE Transactions on Power Systems, Vol. 11, No. 3, pp. 1441-1450, August 1996. https://doi.org/10.1109/59.535685
  8. C.A. Canizares, F.L. Alvarado, C.L. DeMarco, I. Dobson and W.F. Long, "Point Of Collapse Methods Applied to Ac/Dc Power Systems," IEEE Transactions on Power Systems, Vol. 7, No. 2, pp. 673-683, May 1992.
  9. V. Ajjarapu and C. Christy, "The Continuation Power Flow: A Tool for Steady State Voltage Stability Analysis," IEEE Transactions on Power Systems, Vol. 7, No.1, pp. 416-423, Feb. 1992.
  10. G. K. Morison, B. Gao, and P. Kundur, "Voltage Stability Analysis using Static and Dynamic Approaches," IEEE Transactions on Power Systems, Vol. 8, No. 3, pp. 1159-1165, August 1993. https://doi.org/10.1109/59.260881
  11. M.K.Pal, "Voltage Stability Conditions Considering Load Characteristics," IEEE Transactions on Power Systems, Vol. 7, No. 1, pp. 243-249, Feb. 1992. https://doi.org/10.1109/59.141710
  12. Karlsson and D. J. Hill, "Modeling and Identification of Nonlinear Dynamic Loads in Power Systems," IEEE Transactions on Power Systems, Vol. 9, No. 1, pp. 157-163, Feb. 1994. https://doi.org/10.1109/59.317546
  13. D. Devaraj, J. Preetha Roselyn and R. Uma Rani, "Artificial Neural Network Model for Voltage Security Based Contingency Ranking," Applied Soft Computing, Vol. 7, No. 3, pp. 722-727, June 2007. https://doi.org/10.1016/j.asoc.2005.11.010
  14. S. Chakrabarti, "Voltage Stability Monitoring by Artificial Neural Network using a Regression-Based Feature Selection Method," Expert Systems with Applications, Vol. 35, No. 4, pp. 1802-1808, Nov. 2008. https://doi.org/10.1016/j.eswa.2007.08.059
  15. D. Devaraj, B. Yegnanarayana and K. Ramar, "Radial Basis Function Networks for Fast Contingency Ranking," Electric Power and Energy Systems Journal, Vol. 24, No. 5, pp. 387-395, June 2002. https://doi.org/10.1016/S0142-0615(01)00041-2
  16. Saikat Chakrabarthi, and Benjamin Jeyasurya, "Multi-Contingency Voltage Stability Monitoring of a Power System using an Adaptive Radial Basis Function Network," Electric Power and Energy Systems Journal, Vol. 30, No.1, pp. 1-7, Jan. 2008. https://doi.org/10.1016/j.ijepes.2007.06.004
  17. Jayashankar V, Kamaraj N, and Vanaja N, "Estimation of Voltage Stability Index for Power System Employing Artificial Neural Network Technique and TCSC Placement," Neurocomputing, Vol. 73, No. 16-83, pp. 3005-3011, Oct. 2010. https://doi.org/10.1016/j.neucom.2010.07.006
  18. D. Devaraj and J. Preetha Roselyn, "On-Line Voltage Stability Assessment using Radial Basis Function Network Model with Reduced Input Features," Electrical Power and Energy Systems Journal, Vol. 33, No. 9, pp: 1550-1555, Nov. 2011. https://doi.org/10.1016/j.ijepes.2011.06.008
  19. P. Aravindhababu, G. Balamurugan, "ANN based Online Voltage Estimation," Applied Soft Computing, Vol. 12, No.1, pp. 313-319, Jan. 2012. https://doi.org/10.1016/j.asoc.2011.08.041
  20. T. L. Baldwin, L. Mili, M. B. Boisen, Jr, and R. Adapa, "Power System Observability with Minimal Phasor Measurement Placement," IEEE Trans. Power Syst., Vol. 8, No. 2, pp. 707-715, May 1993. https://doi.org/10.1109/59.260810
  21. Guang-Bin Huang, Qin-Yu Zhu and Chee-Kheong Siew, "Extreme Learning Machine: Theory and Applications," Neurocomputing, Vol. 70, No. 1-3, pp. 489-501, Dec. 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  22. Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, and Rui Zhang, "Extreme Learning Machine for Regression and Multiclass Classification," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 42, No.2, pp. 513-529, April 2012. https://doi.org/10.1109/TSMCB.2011.2168604