Advertisement

Distribution system state estimation: an overview of recent developments

  • Gang Wang
  • Georgios B. Giannakis
  • Jie Chen
  • Jian SunEmail author
Review
  • 3 Downloads

Abstract

In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

Key words

State estimation Cramér-Rao bound Feasible point pursuit Semidefinite relaxation Proximal linear algorithm Composite optimization Cyber attack Bad data detection 

CLC number

TP311 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abur A, Celik MK, 1991. A fast algorithm for the weighted least-absolute-value state estimation (for power systems). IEEE Trans Power Syst, 6(1):1–8. https://doi.org/10.1109/59.131040Google Scholar
  2. Abur A, Gómez-Expósito A, 2004. Power System State Estimation: Theory and Implementation. Marcel Dekker, New York, USA.Google Scholar
  3. Aghamolki HG, Miao Z, Fan L, 2018. SOCP convex relaxation-based simultaneous state estimation and bad data identification. https://arxiv.org/abs/1804.05130Google Scholar
  4. Ahmad F, Rasool A, Ozsoy E, et al., 2018. Distribution system state estimation—a step towards smart grid. Renew Sust Energ Rev, 81:2659–2671. https://doi.org/10.1016/j.rser.2017.06.071Google Scholar
  5. Baran ME, 2001. Challenges in state estimation on distribution systems. Power Engineering Society Summer Meeting, p.429–433. https://doi.org/10.1109/PESS.2001.970062Google Scholar
  6. Ben-Tal A, Nemirovski A, 2001. Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications. SIAM, Philadelphia, USA.zbMATHGoogle Scholar
  7. Bertsekas DP, 1999. Nonlinear Programming. Athena Scientific, Belmont, Massachusetts, USA.zbMATHGoogle Scholar
  8. Bhela S, Kekatos V, Veeramachaneni S, 2018. Enhancing observability in distribution grids using smart meter data. IEEE Trans Smart Grid, 9(6):5953–5961. https://doi.org/10.1109/TSG.2017.2699939Google Scholar
  9. Burke JV, Ferris MC, 1995. A Gauss-Newton method for convex composite optimization. Math Programm, 71(2):179–194. https://doi.org/10.1007/BF01585997MathSciNetzbMATHGoogle Scholar
  10. Candès EJ, Li X, Soltanolkotabi M, 2015. Phase retrieval via Wirtinger flow: theory and algorithms. IEEE Trans Inform Theory, 61(4):1985–2007. https://doi.org/10.1109/TIT.2015.2399924MathSciNetzbMATHGoogle Scholar
  11. Caro E, Conejo A, 2012. State estimation via mathematical programming: a comparison of different estimation algorithms. IET Gener Transm Distrib, 6(6):545–553. https://doi.org/10.1049/iet-gtd.2011.0663Google Scholar
  12. Christie RD, 1999. Power Systems Test Case Archive. University of Washington. https://labs.ece.uw.edu/pstca/Google Scholar
  13. Clements KA, 2011. The impact of pseudo-measurements on state estimator accuracy. IEEE Power and Energy Society General Meeting, p.1–4. https://doi.org/10.1109/PES.2011.6039370Google Scholar
  14. Della Giustina D, Pau M, Pegoraro PA, et al., 2014. Electrical distribution system state estimation: measurement issues and challenges. IEEE Instrum Meas Mag, 17(6):36–42. https://doi.org/10.1109/MIM.2014.6968929Google Scholar
  15. Duchi JC, Ruan F, 2017a. Solving (most) of a set of quadratic equalities: composite optimization for robust phase retrieval. Inform Infer J IMA, iay015. https://doi.org/10.1093/imaiai/iay015Google Scholar
  16. Duchi JC, Ruan F, 2017b. Stochastic methods for composite optimization problems. https://arxiv.org/abs/1703.08570zbMATHGoogle Scholar
  17. Džafić I, Jabr RA, Hrnjić T, 2018a. High performance distribution network power flow using Wirtinger calculus. IEEE Trans Smart Grid, in press. https://doi.org/10.1109/TSG.2018.2824018Google Scholar
  18. Džafić I, Jabr RA, Hrnjić T, 2018b. Hybrid state estimation in complex variables. IEEE Trans Power Syst, 33(5):5288–5296. https://doi.org/10.1109/TPWRS.2018.2794401Google Scholar
  19. Fairley P, 2016. Cybersecurity at US utilities due for an upgrade: tech to detect intrusions into industrial control systems will be mandatory. IEEE Spectr, 53(5):11–13. https://doi.org/10.1109/MSPEC.2016.7459104Google Scholar
  20. Fletcher R, Watson GA, 1980. First and second order conditions for a class of nondifferentiable optimization problems. Math Programm, 18(1):291–307. https://doi.org/10.1007/BF01588325MathSciNetzbMATHGoogle Scholar
  21. Giannakis GB, Kekatos V, Gatsis N, et al., 2013. Monitoring and optimization for power grids: a signal processing perspective. IEEE Signal Process Mag, 30(5):107–128. https://doi.org/10.1109/MSP.2013.2245726Google Scholar
  22. Göl M, Abur A, 2014. LAV based robust state estimation for systems measured by PMUs. IEEE Trans Smart Grid, 5(4):1808–1814. https://doi.org/10.1109/TSG.2014.2302213Google Scholar
  23. Huang YF, Werner S, Huang J, et al., 2012. State estimation in electric power grids: meeting new challenges presented by the requirements of the future grid. IEEE Signal Process Mag, 29(5):33–43. https://doi.org/10.1109/MSP.2012.2187037Google Scholar
  24. Huber PJ, 2011. Robust Statistics. In: Lovric M (Ed.), International Encyclopedia of Statistical Science. Springer, Berlin, p.1248–1251.Google Scholar
  25. Jabr R, Pal B, 2003. Iteratively re-weighted least-absolutevalue method for state estimation. IET Gener Transm Distrib, 150(4):385–391. https://doi.org/10.1049/ip-gtd:20030462Google Scholar
  26. Jabr R, Pal B, 2004. Iteratively reweighted least-squares implementation of the WLAV state-estimation method. IET Gener Transm Distrib, 151(1):103–108. https://doi.org/10.1049/ip-gtd:20040030Google Scholar
  27. Kay SM, 1993. Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory. Prentice Hall, Englewood Cliffs, USA.zbMATHGoogle Scholar
  28. Kekatos V, Giannakis GB, 2013. Distributed robust power system state estimation. IEEE Trans Power Syst, 28(2):1617–1626. https://doi.org/10.1109/TPWRS.2012.2219629Google Scholar
  29. Kekatos V, Wang G, Zhu H, et al., 2017. PSSE redux: convex relaxation, decentralized, robust, and dynamic approaches. https://arxiv.org/abs/1708.03981Google Scholar
  30. Kim SJ, Wang G, Giannakis GB, 2014. Online semidefinite programming for power system state estimation. IEEE Conf on Acoustics, Speech, and Signal Process, p.6024–6027. https://doi.org/10.1109/ICASSP.2014.6854760Google Scholar
  31. Kosut O, Jia L, Thomas J, et al., 2011. Malicious data attacks on the smart grid. IEEE Trans Smart Grid, 2(4):645–658. https://doi.org/10.1109/TSG.2011.2163807Google Scholar
  32. Kotiuga WW, Vidyasagar M, 1982. Bad data rejection properties of weighted least-absolute-value techniques applied to static state estimation. IEEE Trans Power Appar Syst, 101(4):844–853. https://doi.org/10.1109/TPAS.1982.317150Google Scholar
  33. Kreutz-Delgado K, 2009. The complex gradient operator and the CR-calculus. https://arxiv.org/abs/0906.4835Google Scholar
  34. Lewis AS, Wright SJ, 2016. A proximal method for composite minimization. Math Programm, 158(1-2):501–546. https://doi.org/10.1007/s10107-015-0943-9MathSciNetzbMATHGoogle Scholar
  35. Liu Y, Ning P, Reiter MK, 2011. False data injection attacks against state estimation in electric power grids. ACM Trans Inform Syst Sec, 14(1):1–33. https://doi.org/10.1145/1952982.1952995Google Scholar
  36. Lu C, Teng J, Liu WH, 1995. Distribution system state estimation. IEEE Trans Power Syst, 10(1):229–240. https://doi.org/10.1109/59.373946Google Scholar
  37. Mehanna O, Huang K, Gopalakrishnan B, et al., 2015. Feasible point pursuit and successive approximation of nonconvex QCQPs. IEEE Signal Process Lett, 22(7):804–808. https://doi.org/10.1109/LSP.2014.2370033Google Scholar
  38. Mili L, Cheniae MG, Rousseeuw PJ, 1994. Robust state estimation of electric power systems. IEEE Trans Circ Syst I Fundam Theory Appl, 41(5):349–358. https://doi.org/10.1109/81.296336zbMATHGoogle Scholar
  39. Monticelli A, 2000. Electric power system state estimation. Proc IEEE, 88(2):262–282. https://doi.org/10.1109/5.824004Google Scholar
  40. Nesterov Y, 2013. Introductory Lectures on Convex Optimization: a Basic Course. Springer Science & Business Media, Boston, USA.zbMATHGoogle Scholar
  41. Pardalos PM, Vavasis SA, 1991. Quadratic programming with one negative eigenvalue is NP-hard. J Glob Optim, 1(1):15–22. https://doi.org/10.1007/BF00120662MathSciNetzbMATHGoogle Scholar
  42. Park J, Boyd S, 2017. General heuristics for nonconvex quadratically constrained quadratic programming. https://arxiv.org/abs/1703.07870Google Scholar
  43. Saad Y, 2003. Iterative Methods for Sparse Linear Systems (2nd Ed.). Society for Industrial and Applied Mathematics, Philadelphia, USA.zbMATHGoogle Scholar
  44. Schweppe FC, Wildes J, Rom D, 1970. Power system static state estimation: parts I, II, and III. IEEE Trans Power Appar Syst, 89(1):120–135.Google Scholar
  45. Singh R, Pal B, Jabr R, 2009. Choice of estimator for distribution system state estimation. IET Gener Transm Distrib, 3(7):666–678. https://doi.org/10.1049/iet-gtd.2008.0485Google Scholar
  46. Stoica P, Marzetta TL, 2001. Parameter estimation problems with singular information matrices. IEEE Trans Signal Process, 49(1):87–90. https://doi.org/10.1109/78.890346MathSciNetzbMATHGoogle Scholar
  47. Wang G, Kim SJ, Giannakis GB, 2014. Moving-horizon dynamic power system state estimation using semidefinite relaxation. IEEE PES General Meeting & Conf Exposition, p.1–5. https://doi.org/10.1109/PESGM.2014.6939925Google Scholar
  48. Wang G, Zamzam AS, Giannakis GB, et al., 2016. Power system state estimation via feasible point pursuit. IEEE Global Conf Signal and Information Process, p.773–777. https://doi.org/10.1109/GlobalSIP.2016.7905947Google Scholar
  49. Wang G, Giannakis GB, Chen J, 2017. Robust and scalable power system state estimation via composite optimization. https://arxiv.org/abs/1708.06013Google Scholar
  50. Wang G, Giannakis GB, Saad Y, et al., 2018a. Phase retrieval via reweighted amplitude flow. IEEE Trans Signal Process, 66(11):2818–2833. https://doi.org/10.1109/TSP.2018.2818077MathSciNetGoogle Scholar
  51. Wang G, Zamzam AS, Giannakis GB, et al., 2018b. Power system state estimation via feasible point pursuit: algorithms and Cramér-Rao bound. IEEE Trans Signal Process, 66(6):1649–1658. https://doi.org/10.1109/TSP.2018.2791977MathSciNetGoogle Scholar
  52. Wang G, Zhu H, Giannakis GB, et al., 2018c. Robust power system state estimation from rank-one measurements. IEEE Trans Contr Netw Syst, in press. https://doi.org/10.1109/TCNS.2019.2890954Google Scholar
  53. Wang G, Giannakis GB, Eldar YC, 2018d. Solving systems of random quadratic equations via truncated amplitude flow. IEEE Trans Inform Theory, 64(2):773–794. https://doi.org/10.1109/TIT.2017.2756858MathSciNetzbMATHGoogle Scholar
  54. Wang Z, Cui B, Wang J, 2017. A necessary condition for power flow insolvability in power distribution systems with distributed generators. IEEE Trans Power Syst, 32(2):1440–1450. https://doi.org/10.1109/TPWRS.2016.2588341Google Scholar
  55. Wood AJ, Wollenberg BF, 1996. Power Generation, Operation, and Control (2nd Ed.). Wiley & Sons, New York, USA.Google Scholar
  56. Wulf WA, 2000. Great achievements and grand challenges. Nat Acad Eng, 30(1):5–10.MathSciNetGoogle Scholar
  57. Zamzam AS, Fu X, Sidiropoulos ND, 2018. Data-driven learning-based optimization for distribution system state estimation. https://arxiv.org/abs/1807.01671Google Scholar
  58. Zhang L, Wang G, Giannakis GB, 2017. Going beyond linear dependencies to unveil connectivity of meshed grids. IEEE 7th Workshop on Computational Advances in Multi-sensor Adaptive Processing, p.1–5. https://doi.org/10.1109/CAMSAP.2017.8313078Google Scholar
  59. Zhang L, Wang G, Giannakis GB, 2018a. Real-time power system state estimation via deep unrolled neural networks. IEEE Global Conf on Signal and Information Processing, in press.Google Scholar
  60. Zhang L, Wang G, Giannakis GB, 2018b. Real-time power system state estimation and forecasting via deep neural networks. https://arxiv.org/abs/1811.06146Google Scholar
  61. Zhang L, Wang G, Giannakis GB, 2019. Power system state forecasting via deep recurrent neural networks. IEEE Conf on Acoustics, Speech, and Signal Process, in press.Google Scholar
  62. Zhu H, Giannakis GB, 2011. Estimating the state of AC power systems using semidefinite programming. North American Power Symp, p.1–7. https://doi.org/10.1109/NAPS.2011.6024862Google Scholar
  63. Zhu H, Giannakis GB, 2012. Robust power system state estimation for the nonlinear AC flow model. North American Power Symp, p.1–6. https://doi.org/10.1109/NAPS.2012.6336405Google Scholar
  64. Zhu H, Giannakis GB, 2014. Power system nonlinear state estimation using distributed semidefinite programming. IEEE J Sel Top Signal Process, 8(6):1039–1050. https://doi.org/10.1109/JSTSP.2014.2331033Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering and Digital Technology CenterUniversity of MinnesotaMinneapolisUSA
  2. 2.School of AutomationBeijing Institute of TechnologyBeijingChina
  3. 3.Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijingChina

Personalised recommendations