Skip to main content
Log in

Modeling of Zero Injection Buses Based to Optimal Placement of PMUs for Full Observability of Power Systems

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

In this research, Zero Injection Buses (ZIBs) were modeled to minimize the number of Phasor Measurements Units (PMUs) and ensure the full observability of power systems. To determine the operation of the proposed method, all ZIBs that are connected to each other and other buses were presented in models H, M, and D, each of which featured new observability constraints. The H set, is defined as the set of buses that are connected to a ZIB. Also, the \(M\) set is defined as the \(H\) set that is connected through ZIBs. The \(D\) set is defined as the \(M\) set that is connected through ZIBs to a common bus. To increase the assurance of complete power system observability in the method put forward in this work, conditions such as line outage, single PMU loss, and line or PMU exit were examined. To demonstrate the efficiency of the approach, it was applied to 14, 30, 39, 57 and 118 IEEE test systems. The simulation results showed that the developed topologies enhanced network observability under a minimum number of PMUs.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Khorram E, Mehdi TJ (2018) PMU placement considering various arrangements of lines connections at complex buses. Int J Electr Power Energy Syst 94:97–103

    Google Scholar 

  2. Nanda P, Panigrahi CK, Dasgupta A (2017) Phasor estimation and modelling techniques of PMU—a review. Energy Procedia 109:64–77

    Google Scholar 

  3. Gomez O, Rios MA, Anders G (2014) Reliability-based phasor measurement unit placement in power systems considering transmission line outages and channel limits. IET Gener Transm Distrib 8(1):121–130

    Google Scholar 

  4. Leelaruji R, Vanfretti L, Uhlen K, Gjerde JO (2015) Computing sensitivities from synchrophasor data for voltage stability monitoring and visualization. Int Trans Electr Energy Syst 25(6):933–947

    Google Scholar 

  5. Sulla F, Koivisto M, Seppänen J, Turunen J, Haarla LC, Samuelsson O (2014) Statistical analysis and forecasting of damping in the nordic power system. IEEE Trans Power Syst 30(1):306–315

    Google Scholar 

  6. Goleijani S, Ameli MT (2018) Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units. Int Trans Electr Energy Syst 28(2):e2481

    Google Scholar 

  7. Manousakis NM, Korres GN (2018) A hybrid power system state estimator using synchronized and unsynchronized sensors. Int Trans Electr Energy Syst 28(8):e2580

    Google Scholar 

  8. Pal A, Kumar A, Vullikanti S, Ravi SS (2016) A PMU placement scheme considering realistic costs and modern trends in relaying. IEEE Trans Power Syst 32(1):552–561

    Google Scholar 

  9. Pal A, Mishra C, Kumar A, Vullikanti S, Ravi SS (2017) General optimal substation coverage algorithm for phasor measurement unit placement in practical systems. IET Gene Transm Distrib 11(2):347–353

    Google Scholar 

  10. Rashidi F, Abiri E, Niknam T, Salehi MR (2015) Optimal placement of PMUs with limited number of channels for complete topological observability of power systems under various contingencies. Int J Electr Power Energy Syst 67:125–137

    Google Scholar 

  11. Aminifar F, Khodaei A, Fotuhi-Firuzabad M, Shahidehpour M (2009) Contingency-constrained PMU placement in power networks. IEEE Trans Power Syst 25(1):516–523

    Google Scholar 

  12. Mahaei SM, Tarafdar-Hagh M (2012) Minimizing the number of PMUs and their optimal placement in power systems. Electr Power Syst Res 83(1):66–72

    Google Scholar 

  13. Peng J, Sun Y, Wang HF (2006) Optimal PMU placement for full network observability using Tabu search algorithm. Int J Electr Power Energy Syst 28(4):223–231

    Google Scholar 

  14. Nuqui RF, Phadke AG (2005) Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Trans Power Deliv 20(4):2381–2388

    Google Scholar 

  15. Mousavian S, Feizollahi MJ (2015) An investment decision model for the optimal placement of phasor measurement units. Expert Syst Appl 42(21):7276–7284

    Google Scholar 

  16. Aminifar F, Lucas C, Khodaei A, Fotuhi-Firuzabad M (2009) Optimal placement of phasor measurement units using immunity genetic algorithm. IEEE Trans Power Deliv 24(3):1014–1020

    Google Scholar 

  17. Rather ZH, Chen Z, Thøgersen P, Lund P, Kirby B (2014) Realistic approach for phasor measurement unit placement: Consideration of practical hidden costs. IEEE Trans Power Deliv 30(1):3–15

    Google Scholar 

  18. Mishra C, Jones KD, Pal A, Centeno VA (2016) Binary particle swarm optimisation-based optimal substation coverage algorithm for phasor measurement unit installations in practical systems. IET Gener Transm Distrib 10(2):555–562

    Google Scholar 

  19. Basetti V, Chandel AK (2017) Optimal PMU placement for power system observability using Taguchi binary bat algorithm. Measurement 95:8–20

    Google Scholar 

  20. Gao Y, Hu Z, He X, Liu D (2008) Optimal placement of PMUs in power systems based on improved PSO algorithm. In: 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp 2464–2469. IEEE, 2008.

  21. Li W, Deka D, Chertkov M, Wang M (2019) Real-time faulted line localization and PMU placement in power systems through convolutional neural networks. IEEE Trans Power Syst 34(6):4640–4651.https://doi.org/10.1109/TPWRS.2019.2917794

    Article  Google Scholar 

  22. Dua D, Dambhare S, Gajbhiye RK, Soman SA (2008) Optimal multistage scheduling of PMU placement: An ILP approach. IEEE Trans Power Deliv 23(4):1812–1820

    Google Scholar 

  23. Gou B (2008) Generalized integer linear programming formulation for optimal PMU placement. IEEE Trans Power Syst 23(3):1099–1104

    Google Scholar 

  24. Hajian M, Ranjbar AM, Amraee T, Mozafari B (2011) Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm. Int J Electrl Power Energy Syst 33(1):28–34

    Google Scholar 

  25. Khajeh KG, Bashar E, Rad AM, Gharehpetian GB (2015) Integrated model considering effects of zero injection buses and conventional measurements on optimal PMU placement. IEEE Trans Smart Grid 8(2):1006–1013

    Google Scholar 

  26. Gou B (2008) Optimal placement of PMUs by integer linear programming. IEEE Trans Power Syst 23(3):1525–1526

    Google Scholar 

  27. ‘MatPower Software Package’. https://www.pserc.cornell.edu/matpower. Accessed online

  28. Abbasy NH, Ismail HM (2009) A unified approach for the optimal PMU location for power system state estimation. IEEE Trans Power Syst 24(2):806–813

    Google Scholar 

  29. Roy BKS, Sinha AK, Pradhan AK (2012) An optimal PMU placement technique for power system observability. Int J Electr Power Energy Syst 42(1):71–77

    Google Scholar 

  30. Esmaili M, Gharani K, Shayanfar HA (2013) Redundant observability PMU placement in the presence of flow measurements considering contingencies. IEEE Trans Power Syst 28(4):3765–3773

    Google Scholar 

  31. Mazhari SM, Monsef H, Lesani H, Fereidunian A (2013) A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies. IEEE Trans Power Syst 28(3):2136–2146

    Google Scholar 

  32. Bei X, Yoon YJ, Abur A (2005) Optimal placement and utilization of phasor measurements for state estimation. PSERC Publication, New York, p 1

    Google Scholar 

  33. Lu C, Wang Z, Ma M, Shen R, Yang Yu (2018) An optimal PMU placement with reliable zero injection observation. IEEE Access 6:54417–54426

    Google Scholar 

  34. Babu R, Bhattacharyya B (2018) An approach for optimal placement of phasor measurement unit for power network observability considering various contingencies. Iran J Sci Technol Trans Electr Eng 42(2):161–183

    Google Scholar 

  35. Abdulrahman I, Radman G (2018) ILP-based optimal PMU placement with the inclusion of the effect of a group of zero-injection buses. J Control Autom Electr Syst 29(4):512–524

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aref Jalili Irani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niyaragh, S.M.M., Irani, A.J. & Shayeghi, H. Modeling of Zero Injection Buses Based to Optimal Placement of PMUs for Full Observability of Power Systems. J. Electr. Eng. Technol. 15, 2509–2518 (2020). https://doi.org/10.1007/s42835-020-00536-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-020-00536-0

Keywords

Navigation