Skip to main content
Log in

Meter Placement in Active Distribution System using Objective Discretization and Indicator-Based Multi-Objective Evolutionary Algorithm with Adaptive Reference Point Method

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

A new indicator-based multi-objective evolutionary algorithm (MOEA) using the objective discretization method is proposed for the meter placement problem (MPP) in active distribution system. Because MPP is a combinatorial optimization, a combination of measurement sets produces a discrete objective space. Therefore, the objective discretization method has been adopted to enhance the performance of MOEA. The proposed MOEA is an indicator-based method using an inverted generational distance indicator with noncontributing solution detection (IGD-NS) and with an adaptive reference point method (IB-MOEA-AR). The advantage of the IGD-NS indicator is that it measures the diversity and convergence of the solution set as well as identifies the solutions, which does not contribute to the indicator. As the performance of MOEA mostly depends on the Pareto front shape, the proposed method employs an adaptive reference point approach to follow the shape of the Pareto front. Moreover, the effect of distributed generation is investigated on distribution system state estimation performance for different measurement uncertainties as well as for various distributed renewable generations. The MPP is modeled as a multi-objective problem with the objectives consisting of minimization of total meter cost and state estimation errors. The versatility of the proposed method is demonstrated on Indian Practical 85-bus distribution system and UKGDS 95-bus distribution system. The results obtained are compared to existing MOEAs in the literature, to demonstrate the superiority of the proposed method over other methods.

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.

Fig. 1
Fig.2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. R.Z. Fanucchi, M. Bessani, M.H.M. Camillo, A.S. da Soares, B.A. João, Stochastic indexes for power distribution systems resilience analysis. IET Gener. Transm. Distrib. 13(12), 2507–2516 (2019)

    Article  Google Scholar 

  2. V.A. Evangelopoulos, P.S. Georgilakis, N.D. Hatziargyriou, Optimal operation of smart distribution networks: a review of models, methods and future research. Electr. Power Syst. Res. 14(12), 95–106 (2016)

    Article  Google Scholar 

  3. A. Angioni, T. Schlsser, F. Ponci, A. Monti, Impact of pseudo measurements from new power profiles on state estimation in low voltage grids. IEEE Trans. Instrum. Meas. 65(1), 70–77 (2016)

    Article  Google Scholar 

  4. R.A. Jabr, B.C. Pal, R. Singh, Choice of estimator for distribution system state estimation. IET Gener. Transm. Distrib. 3(7), 666–678 (2009)

    Article  Google Scholar 

  5. M.E. Baran, J. Zhu, A.W. Kelley, Meter placement for real-time monitoring of distribution feeders. IEEE Trans. Power Syst. 11(1), 332–337 (1996)

    Article  Google Scholar 

  6. C. Muscas, F. Pilo, G. Pisano, S. Sara, Optimal allocation of multichannel measurement devices for distribution state estimation. IEEE Trans. Instrum. Meas. 58(6), 1929–1937 (2009)

    Article  Google Scholar 

  7. P.A. Pegoraro, S. Sulis, Robustness-oriented meter placement for distribution system state estimation in presence of network parameter uncertainty. IEEE Trans. Instrum. Meas. 62(5), 954–962 (2013)

    Article  Google Scholar 

  8. R. Sing, B.C. Pal, R.A. Jabr, B. Richard, Vinter, meter placement for distribution system state estimation: an ordinal optimization approach. IEEE Trans. Power Syst. 26(4), 2328–2335 (2011)

    Article  Google Scholar 

  9. K. Chauhan, R. Sodhi, Placement of distribution-level phasor measurements for topological observability and monitoring of active distribution networks. IEEE Trans. Instrum. Meas. (2019). https://doi.org/10.1109/TIM.2019.2939951

    Article  Google Scholar 

  10. M.G. Damavandi, V. Krishnamurthy, J.R. Marti, Robust meter placement for state estimation in active distribution systems. IEEE Trans. Smart Grid 6(4), 1972–1982 (2015)

    Article  Google Scholar 

  11. R. Sing, B.C. Pal, R.B. Vinter, Measurement placement in distribution system state estimation. IEEE Trans. Power Syst. 24(2), 668–675 (2009)

    Article  Google Scholar 

  12. K. Dehghanpour, Z. Wang, J. Wang, Y. Yuan, Bu. Fankun, A survey on state estimation techniques and challenges in smart distribution systems. IEEE Trans. Smart Grid 10(2), 2312–2322 (2019)

    Article  Google Scholar 

  13. F. Ahmad, A. Rasool, S. Emre Ozsoy, A.S. Rajasekar, M. Elitas, Distribution system state estimation-a step towards smart grid. Renew. Sustain. Energy Rev. 81(1), 2659–2671 (2018)

    Article  Google Scholar 

  14. A. Zhou et al., Multi-objective evolutionary algorithms: a survey of the state of the art. Swarm E Comput. 1(1), 32–49 (2011)

    Article  Google Scholar 

  15. J. Liu, F. Ponci, A. Monti, C. Muscas, P.A. Pegoraro, S. Sulis, Optimal meter placement for robust measurement systems in active distribution grids. IEEE Trans. Instrum. Meas. 63(5), 1096–1105 (2014)

    Article  Google Scholar 

  16. A. Shafiu, N. Jenkins, G. Strbac, Measurement location for state estimation of distribution networks with generation. IEEE Proc. Gener. Transm. Distrib. 152(2), 240–246 (2005)

    Article  Google Scholar 

  17. J. Liu, J. Tang, F. Ponci, A. Monti, C. Muscas, P.A. Pegoraro, Trade-offs in PMU deployment for state estimation in active distribution grids. IEEE Trans. Smart Grid 3(2), 915–924 (2012)

    Article  Google Scholar 

  18. M. Pau, P.A. Pegoraro, S. Sulis, Efficient branch-current-based distribution system state estimation including synchronized measurement. IEEE Trans. Instrum. Meas. 62(9), 2419–2429 (2013)

    Article  Google Scholar 

  19. X. Chen, J. Lin, C. Wan, Y. Song, S. You, Yi. Zong, W. Guo, Y. Li, Optimal meter placement for distribution network state estimation: a circuit representation-based MILP approach. IEEE Trans. Power Syst. 31(6), 4357–4370 (2016)

    Article  Google Scholar 

  20. S. Prasad, D.M. Vinod Kumar, Optimal allocation of measurement devices for distribution state estimation using multi-objective hybrid PSO–krill herd algorithm. IEEE Trans. Instrum. Meas. 66(8), 2022–2035 (2017)

    Article  Google Scholar 

  21. S. Prasad, D.M. Vinod Kumar, Multi-objective hybrid estimation of distribution algorithm-interior point method-based meter placement for active distribution state estimation. IET Gener. Transm. Distrib. 12(3), 767–779 (2018)

    Article  Google Scholar 

  22. S. Prasad, D.M. Vinod Kumar, Trade-offs in PMU and IED deployment for active distribution state estimation using multi-objective evolutionary algorithm. IEEE Trans. Instrum. Meas. 67(6), 1298–1307 (2018)

    Article  Google Scholar 

  23. S. Prasad, D.M. Vinod Kumar, Robust meter placement for active distribution state estimation using a new multi-objective optimization model. IET Sci. Meas. Technol. 12(8), 1047–1057 (2018)

    Article  Google Scholar 

  24. R.C. Purshouse, P.J. Fleming, On the evolutionary optimization of many conflicting objectives. IEEE Trans. E Comput. 11(6), 770–784 (2007)

    Article  Google Scholar 

  25. K. Li, K. Deb, Q. Zhang, S. Kwong, an evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evolut. Comput. 19(5), 694–716 (2015)

    Article  Google Scholar 

  26. H. Ishibuchi, Y. Setoguchi, H. Masuda, Y. Nojima, Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes. IEEE Trans. E Comput. 21(2), 169–190 (2017)

    Article  Google Scholar 

  27. W. Chen, H. Ishibuchi, K. Shang, Effects of discretization of decision and objective spaces on the performance of evolutionary multi- objective optimization algorithms, IEEE Symposium Series on Computational Intelligence (SSCI), (Xiamen, China, 2019)

  28. H. Ishibuchi, M. Yamane, Y. Nojima, Difficulty in evolutionary multiobjective optimization of discrete objective functions with different granularities, Lecture notes in computer science 7811: evolutionary multi-criterion optimization, (Springer, Berlin, 2013), pp.230–245

  29. M. Li, S. Yang, X. Liu, Pareto or non-pareto: Bi-criterion evolution in multiobjective optimization. IEEE Trans. E Comput. 20(5), 645–665 (2016)

    Article  Google Scholar 

  30. H. Wang, N.N. Schulz, A revised branch current-based distribution system state estimation algorithm and meter placement impact. IEEE Trans. Power Syst. 19(1), 207–213 (2004)

    Article  Google Scholar 

  31. M. Pau, P.A. Pegoraro, S. Sulis, Efficient branch-current-based distribution system state estimation including synchronized measurements. IEEE Trans. Instrum. Meas. 62(9), 2419–2429 (2013)

    Article  Google Scholar 

  32. Y. Tian, R. Cheng, X. Zhang, F. Cheng, Y. Jin, An indicator-based multi-objective evolutionary algorithm with reference point adaption for better versatility. IEEE Trans. E Comput. 22(4), 609–622 (2018)

    Article  Google Scholar 

  33. L. While, P. Hingston, L. Barone, S. Huband, A faster algorithm for calculating hypervolume. IEEE Trans. E Comput. 10(1), 29–38 (2006)

    Article  Google Scholar 

  34. D. Brockhoff, T. Wagner, H. Trautmann, On the properties of the R2 indicator, in Proc. 14th Annu. Conf. Genet. E Comput., (Philadelphia, PA, USA, 2012) pp. 465–472

  35. A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, E. Tsang, Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion, in Proc. IEEE Congr. E Comput., (Vancouver, BC, Canada, 2006) pp. 892–899

  36. I. Das, J.E. Dennis, Normal-boundary intersection: a new method for generating Pareto optimal points in multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)

    Article  MathSciNet  Google Scholar 

  37. R. Cheng, Y. Jin, M. Olhofer, B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. E Comput. 20(5), 773–791 (2016)

    Article  Google Scholar 

  38. X. Zhang, Y. Tian, R. Cheng, Y. Jin, An efficient approach to nondominated sorting for evolutionary multi-objective optimization. IEEE Trans. E Comput. 19(2), 201–213 (2015)

    Article  Google Scholar 

  39. S. Kayalvizhi, D. M. V. Kumar, Dispatchable DG planning in distribution networks considering costs, in Proc. IEEE Int. Conf. Recent Develop. Control, Autom. Power Eng. (2016)

  40. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  41. D. Das, D.P. Kothari, A. Kalam, Simple and efficient method for load flow solution of radial distribution networks. Int. J. Electr. Power Energy Syst. 17(5), 335–346 (1995)

    Article  Google Scholar 

Download references

Funding

No funding is received for this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Bhanu Prasad.

Ethics declarations

Conflict of interest

The authors declare that there are no known conflicts of interest associated with this Manuscript and there has been no significant financial support for this work that could have influenced its outcome.

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

Prasad, C.B., Kumar, D.M.V. Meter Placement in Active Distribution System using Objective Discretization and Indicator-Based Multi-Objective Evolutionary Algorithm with Adaptive Reference Point Method. J. Inst. Eng. India Ser. B 103, 887–901 (2022). https://doi.org/10.1007/s40031-021-00703-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-021-00703-5

Keywords

Navigation