Skip to main content
SpringerLink
Log in

Error Detection of DC Power Flow Using State Estimation

  • Chapter
  • First Online:
Smart Grids: Security and Privacy Issues

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. V.C. Gungor et al., Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7 (4), 529–539 (2011)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 1–5

    Google Scholar 

  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 2015

    Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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 2015

    Google Scholar 

  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 2010

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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 2013

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 1–4

    Google Scholar 

  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. 1–5

    Google Scholar 

  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. 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. 924–929

    Google Scholar 

  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, 2013

    Google Scholar 

  19. Y. Shoham, K. Leyton-Brown, Multi-Agent Systems: Algorithmic. Game Theoretic and Logical Foundations. (Cambridge University Press, Cambridge, 2009–2010)

    Google Scholar 

  20. F. Bellifemine, G. Caire, D. Greenwood, Developing Multi-agent systems with JADE (Wiley, New York, 2007)

    Book  Google Scholar 

  21. M. Wooldridge, G. Weiss, Intelligent agents, in Multi-Agent Systems (MIT Press, Cambridge, MA, 1999), pp. 3–51

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  24. J.D. Glover, M.S. Sarma, Power System Analysis and Design, 3rd edn. (Pacific Grove, CA, Brooks/Cole, 2002)

    Google Scholar 

  25. B. Stott, Review of load-flow calculation methods. Proc. IEEE 62, 916–929 (1974)

    Article  Google Scholar 

  26. A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control, 2nd edn. (Wiley, New York, 1996)

    Google Scholar 

  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)

    Article  Google Scholar 

  28. L. Powell, DC load flow, Chap. 11, in Power System Load Flow Analysis. McGrawHill Professional Series (McGrawHill, New York, 2004)

    Google Scholar 

  29. B. Stott, J. Jardim, O. Alsac, DC power flow revisited. IEEE Trans. Power Syst. 24 (3), 1290–1300 (2009)

    Article  Google Scholar 

  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. R. Baldick, Variation of distribution factors with loading. IEEE Trans. Power Syst. 18 (4), 1316–1323 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  33. M.D. Ilić, J. Zaborszky, Dynamics and Control of Large Electric Power Systems (Wiley, New York, 2000)

    Google Scholar 

  34. S.M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, 1st edn. (Prentice-Hall International Editions, Englewood Cliffs, 1993)

    MATH  Google Scholar 

  35. F.F. Wu, K. Moslehi, A. Bose, Power system control centers: past, present, and future. Proc. IEEE 93 (11), 1890–1908 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  37. A. Abur, A.G. Exposito, Power System State Estimation: Theory and Implementation (CRC Press, New York, 2002)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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, 2014

    Google Scholar 

  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 2015

    Google Scholar 

  42. M.H. Amini et al., Sparsity-based error detection in DC power flow state estimation. arXiv preprint arXiv:1605.04380, 2016

    Google Scholar 

  43. R.L. Rabiner, R.W. Schafer, Digital Processing of Speech Signals (Prentice Hall, Englewood Cliffs, 1978)

    Google Scholar 

  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, 1986

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  47. O. Edfors et al., OFDM channel estimation by singular value decomposition. IEEE Trans. Commun. 46 (7), 931–939 (1988)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  49. R. Togneri, Estimation theory for engineers, 30 Aug 2005. Online Available: http://staffhome.ecm.uwa.edu.au/00014742/teach/Estimation_Theory.pdf

  50. M. Rahmani, G. Atia, A subspace learning approach to high dimensional matrix decomposition with efficient information sampling. arXiv preprint arXiv:1502.00182, 2016

    Google Scholar 

  51. M. Rahmani, G. Atia, Innovation pursuit: a new approach to subspace clustering. arXiv preprint arXiv:1512.00907, 2015

    Google Scholar 

  52. E. Candes, J. Romberg, Sparsity and incoherence in compressive sampling. Inverse Prob. 23 (3), 969 (2007)

    Google Scholar 

  53. M. Rahmani, G. Atia, High dimensional low rank plus sparse matrix decomposition. arXiv preprint arXiv:1502.00182, 2015

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  56. E.J. Candes, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theory 51 (12) 4203–4215 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  57. University of Washington Electrical Engineering, Power systems test case archive (2015). Online Available: http://www.ee.washington.edu/research/pstca/

Download references

Author information

Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

    Kianoosh G. Boroojeni & S. S. Iyengar

  2. SYSU-CMU Joint Institute of Engineering School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China

    M. Hadi Amini

  3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    M. Hadi Amini

Authors
  1. Kianoosh G. Boroojeni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Hadi Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. S. Iyengar
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Boroojeni, K.G., Amini, M.H., Iyengar, S.S. (2017). Error Detection of DC Power Flow Using State Estimation. In: Smart Grids: Security and Privacy Issues. Springer, Cham. https://doi.org/10.1007/978-3-319-45050-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45050-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45049-0

  • Online ISBN: 978-3-319-45050-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation