Error Detection of DC Power Flow Using State Estimation

  • Kianoosh G. Boroojeni
  • M. Hadi Amini
  • S. S. Iyengar


In recent years, there is an ever-increasing concern about energy consumption and its environmental impacts, reliable energy supply, and sustainable development of energy and power networks. These issues motivate the evolution of Smart Grid (SG) as a novel means to worldwide electricity grid [1]. In this context, optimal operation of the power systems depends on finding the power flow through the transmission lines in the network. DC power flow has been widely used to tackle the power flow problem in the transmission networks.


Smart Grid Power Flow Subspace Cluster Optimal Power Flow Linear Minimum Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    V.C. Gungor et al., Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7 (4), 529–539 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    W. Su, H.R. Eichi, W. Zeng, M.-Y. Chow, A survey on the electrification of transportation in a smart grid environment. IEEE Trans. Ind. Inf. 8 (1), 1–10 (2012)CrossRefGoogle Scholar
  3. 3.
    M.H. Amini, A. Islam Allocation of electric vehicles’ parking lots in distribution network,, in Proceedings of IEEE Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, Feb 2014, pp. 1–5Google Scholar
  4. 4.
    M.H. Amini, O. Karabasoglu, M.D. Ilić, K.G. Boroojeni, S.S. Iyengar, ARIMA-based demand forecasting method considering probabilistic model of electric vehicles’ parking lots. IEEE PES General Meeting 2015, Denver, CO, 26–30 July 2015Google Scholar
  5. 5.
    M.H. Amini, A.I. Sarwat, Optimal reliability-based placement of plug-in electric vehicles in smart distribution network. Int. J. Eng. Sci. 4 (2), 43–49 (2014)Google Scholar
  6. 6.
    J.M. Carrasco et al., Power-electronic systems for the grid integration of renewable energy sources: a survey. IEEE Trans. Ind. Electron. 53 (4), 1002–1016 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    X. Yu, C. Cecati, T. Dilon, M.G. Simoes, The new frontier of smart grids. IEEE Ind. Electron. Mag. 5 (3), 49–63 (2011)CrossRefGoogle Scholar
  8. 8.
    M.H. Amini, J. Frye, Marija D. Ilić, O. Karabasoglu, Smart residential energy scheduling utilizing two stage mixed integer linear programming, in IEEE 47th North American Power Symposium (NAPS 2015), Charlotte, NC, 4–6 Oct 2015Google Scholar
  9. 9.
    National Institute of Standards and Technology, NIST framework and roadmap for smart grid interoperability standards, release 1.0. Office of the National Coordinator for Smart Grid Interoperability-U.S. Department of Commerce, NIST Special Publication 1108, Jan 2010Google Scholar
  10. 10.
    S. Kar, G. Hug, J. Mohammadi, J.M.F. Moura, Distributed state estimation and energy management in smart grids: a consensus+innovations approach. IEEE J. Sel. Top. Sign. Proces. 99, 1–16 (2014)Google Scholar
  11. 11.
    F. Kamyab, M.H. Amini, S. Sheykhha, M. Hasanpour, M.M. Jalali, Demand response program in smart grid using supply function bidding mechanism. IEEE Trans. Smart Grid 7 (3), 1277–1284 (2016)CrossRefGoogle Scholar
  12. 12.
    M.H. Amini, M.P. Moghaddam, Probabilistic modelling of electric vehicles’ parking lots charging demand, in 21th Iranian Conference on Electrical Engineering ICEE2013, Ferdowsi University of Mashhad, 14–16 May 2013Google Scholar
  13. 13.
    A. Zidan, E.F. El-Saadany, A cooperative multi-agent framework for self-healing mechanisms in distribution systems. IEEE Trans. Smart Grid 3 (3), 1525–1539 (2012)CrossRefGoogle Scholar
  14. 14.
    R.E. Brown, Impact of smart grid on distribution system design, in Proceedigs IEEE Power and Energy Society General Meeting, Pittsburgh, PA, July 2008, pp. 1–4Google Scholar
  15. 15.
    M.H. Amini, B. Nabi, M.-R. Haghifam, Load management using multi-agent systems in smart distribution network, in Proceedings of IEEE Power and Energy Society General Meeting, Vancouver, BC, July 2013, pp. 1–5Google Scholar
  16. 16.
    S. Bera, S. Misra, P.C. Rodriguez, Cloud computing applications for smart grid: a survey. IEEE Trans. Parallel Distrib. Syst. 99, 1–18 (2014)Google Scholar
  17. 17.
    C.-T. Yang, W.-S. Chen, K.-L. Huang, J.-C. Liu, W.-H. Hsu, C.-H. Hsu, Implementation of smart power management and service system on cloud computing, in Proceedings of IEEE International Conference on UIC/ATC, 2012, pp. 924–929Google Scholar
  18. 18.
    M. Kezunovic, X. Le, G. Santiago, The role of big data in improving power system operation and protection, in IEEE Bulk Power System Dynamics and Control-IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium, 2013Google Scholar
  19. 19.
    Y. Shoham, K. Leyton-Brown, Multi-Agent Systems: Algorithmic. Game Theoretic and Logical Foundations. (Cambridge University Press, Cambridge, 2009–2010)Google Scholar
  20. 20.
    F. Bellifemine, G. Caire, D. Greenwood, Developing Multi-agent systems with JADE (Wiley, New York, 2007)CrossRefGoogle Scholar
  21. 21.
    M. Wooldridge, G. Weiss, Intelligent agents, in Multi-Agent Systems (MIT Press, Cambridge, MA, 1999), pp. 3–51Google Scholar
  22. 22.
    P. Siano, C. Cecati, H. Yu, J. Kolbusz, Real time operation of smart grids via FCN networks and optimal power flow. IEEE Trans. Ind. Inf., 8 (4), 944–952 (2012)CrossRefGoogle Scholar
  23. 23.
    A.I. Sarwat, M.H. Amini, A. Domijan Jr., A. Damnjanovic, F. Kaleem, Weather-based interruption prediction in the smart grid utilizing chronological data. J. Mod. Power Syst. Clean Energy 4 (2), 308–315 (2016)CrossRefGoogle Scholar
  24. 24.
    J.D. Glover, M.S. Sarma, Power System Analysis and Design, 3rd edn. (Pacific Grove, CA, Brooks/Cole, 2002)Google Scholar
  25. 25.
    B. Stott, Review of load-flow calculation methods. Proc. IEEE 62, 916–929 (1974)CrossRefGoogle Scholar
  26. 26.
    A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control, 2nd edn. (Wiley, New York, 1996)Google Scholar
  27. 27.
    G. Giannakis, V. Kekatos, N. Gatsis, S.-J. Kim, H. Zhu, B. Wollenberg, Monitoring and optimization for power grids: a signal processing perspective. IEEE Signal Process. Mag. 30 (5), 107–128 (2013)CrossRefGoogle Scholar
  28. 28.
    L. Powell, DC load flow, Chap. 11, in Power System Load Flow Analysis. McGrawHill Professional Series (McGrawHill, New York, 2004)Google Scholar
  29. 29.
    B. Stott, J. Jardim, O. Alsac, DC power flow revisited. IEEE Trans. Power Syst. 24 (3), 1290–1300 (2009)CrossRefGoogle Scholar
  30. 30.
    R.J. Kane, F.F. Wu, Flow approximations for steady-state security assessment. IEEE Trans. Circuits Syst. CAS-31 (7), 623–636 (1984)Google Scholar
  31. 31.
    R. Baldick, Variation of distribution factors with loading. IEEE Trans. Power Syst. 18 (4), 1316–1323 (2003)CrossRefGoogle Scholar
  32. 32.
    L. Xiong, W. Peng, L. Pohchiang, A hybrid AC/DC microgrid and its coordination control. IEEE Trans. Smart Grid 2 (2), 278–286 (2011)CrossRefGoogle Scholar
  33. 33.
    M.D. Ilić, J. Zaborszky, Dynamics and Control of Large Electric Power Systems (Wiley, New York, 2000)Google Scholar
  34. 34.
    S.M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, 1st edn. (Prentice-Hall International Editions, Englewood Cliffs, 1993)zbMATHGoogle Scholar
  35. 35.
    F.F. Wu, K. Moslehi, A. Bose, Power system control centers: past, present, and future. Proc. IEEE 93 (11), 1890–1908 (2005)CrossRefGoogle Scholar
  36. 36.
    F.C. Schweppe, J. Wildes, D.B. Rom, Power system static state estimation, Parts I, II and III. IEEE Trans. Power Apparatus Syst. PAS-89 (1), 120–135 (1970)CrossRefGoogle Scholar
  37. 37.
    A. Abur, A.G. Exposito, Power System State Estimation: Theory and Implementation (CRC Press, New York, 2002)Google Scholar
  38. 38.
    P.A. Ruiz, P.W. Sauer, Voltage and reactive power estimation for contingency analysis using sensitivities. IEEE Trans. Power Syst. 22 (2), 639–647 (2007)CrossRefGoogle Scholar
  39. 39.
    K.G. Boroojeni, S. Mokhtari, M.H. Amini, S.S. Iyengar, Optimal two-tier forecasting power generation model in smart grid. Int. J. Inf. Process. 8 (4), 1–10 (2014)Google Scholar
  40. 40.
    M.H. Amini, A.I. Sarwat, S.S. Iyengar, I. Guvenc, Determination of the minimum-variance unbiased estimator for dc power-flow estimation, in 40th IEEE Industrial Electronics Conference (IECON 2014), Dallas, TX, 2014Google Scholar
  41. 41.
    M.H. Amini, M.D. Ilić, O. Karabasoglu, DC power flow estimation utilizing Bayesian-based LMMSE Estimator, in IEEE PES General Meeting 2015, Denver, CO, 26–30 July 2015Google Scholar
  42. 42.
    M.H. Amini et al., Sparsity-based error detection in DC power flow state estimation. arXiv preprint arXiv:1605.04380, 2016Google Scholar
  43. 43.
    R.L. Rabiner, R.W. Schafer, Digital Processing of Speech Signals (Prentice Hall, Englewood Cliffs, 1978)Google Scholar
  44. 44.
    L.R. Bahl et al., Maximum mutual information estimation of hidden Markov model parameters for speech recognition, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP, 1986Google Scholar
  45. 45.
    V. Digalakis, J.R. Rohlicek, M. Ostendorf. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition. IEEE Trans. Speech Audio Process. 1 (4), 431–442 (1993)CrossRefGoogle Scholar
  46. 46.
    V. Tarokh et al., Space-time codes for high data rate wireless communication: performance criteria in the presence of channel estimation errors, mobility, and multiple paths. IEEE Trans. Commun. 47 (2), 199–207 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    O. Edfors et al., OFDM channel estimation by singular value decomposition. IEEE Trans. Commun. 46 (7), 931–939 (1988)CrossRefGoogle Scholar
  48. 48.
    Y. Li, L.J. Cimini Jr., N.R. Sollenberger, Robust channel estimation for OFDM systems with rapid dispersive fading channels. IEEE Trans. Commun. 46 (7), 902–915 (1998)CrossRefGoogle Scholar
  49. 49.
    R. Togneri, Estimation theory for engineers, 30 Aug 2005. Online Available:
  50. 50.
    M. Rahmani, G. Atia, A subspace learning approach to high dimensional matrix decomposition with efficient information sampling. arXiv preprint arXiv:1502.00182, 2016Google Scholar
  51. 51.
    M. Rahmani, G. Atia, Innovation pursuit: a new approach to subspace clustering. arXiv preprint arXiv:1512.00907, 2015Google Scholar
  52. 52.
    E. Candes, J. Romberg, Sparsity and incoherence in compressive sampling. Inverse Prob. 23 (3), 969 (2007)Google Scholar
  53. 53.
    M. Rahmani, G. Atia, High dimensional low rank plus sparse matrix decomposition. arXiv preprint arXiv:1502.00182, 2015Google Scholar
  54. 54.
    E.J. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52 (2), 489–509 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  55. 55.
    E.J. Candes, T. Tao, Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52 (12), 5406–5425 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  56. 56.
    E.J. Candes, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theory 51 (12) 4203–4215 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  57. 57.
    University of Washington Electrical Engineering, Power systems test case archive (2015). Online Available:

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Kianoosh G. Boroojeni
    • 1
  • M. Hadi Amini
    • 2
    • 3
  • S. S. Iyengar
    • 1
  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.SYSU-CMU Joint Institute of Engineering School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations