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μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks

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Abstract

Placement of μPMUs in active distribution networks (ADNs) can significantly improve the system’s observability. Existing research mostly focused on how to achieve global observability with a minimum number of μPMUs, but ignored the system observability under special cases such as line faults, μPMU failures, and topology changes. To ensure ADN’s observability with a minimum number of μPMUs in both normal and special cases, this paper proposes a μPMU placement method that considers the observability of existing monitoring devices, the distribution of observation redundancy, and the nodal priority of μPMUs placement. Simulation results verify that the proposed method needs fewer μPMUs than existing methods and enhances the stability of observability for achieving the global observability of an AND. Both the economic feasibility and observability performance of the PMU placement are considered.

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Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the matpower repository, or are available from the corresponding author upon reasonable request.

References

  1. Wu Z, Yu X, Dong X et al (2018) Real-time situation awareness and evaluation of distribution network based on state estimation. Proc CSU-EPSA 30(03):140–145

    Google Scholar 

  2. Bai L (2009) PMU application prospect in the distribution and the optimized allocation of research. North China Electric Power University.

  3. Qi G, Chunming T, Fei J, Rongwu Z, J Ye, J Gao (2022) An overview of series-connected power electronic converter with function extension strategies in the context of high-penetration of power electronics and renewables. Renew Sustain Energy Rev 156:11934

    Google Scholar 

  4. Qi G, Hou Y, Tu C, Zhou Z, Zhang H, Yu L, Wang L, Jiang J, Wang X (2024) A novel hybrid modular multilevel converter with three-phase coupled high-frequency modules for multi-index optimization. IEEE Trans Transp Electrification. https://doi.org/10.1109/TTE.2024.3379241

    Article  Google Scholar 

  5. Azizi S, Salehi Dobakhshari A, Nezam Sarmadi SA et al (2014) Optimal multi-stage PMU placement in electric power systems using Boolean algebra. Int Trans Electr Energy Syst 24(4):562–577

    Article  Google Scholar 

  6. Dharinee K, Kavitha D, Vijayalakshmi S (2023) Optimum strategy for PMU channel reductions for smart grid data measurement. In: 2023 9th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 2023, pp. 652–658. https://doi.org/10.1109/ICEES57979.2023.10110283.

  7. Milosevic B, Begovic M (2003) Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Trans Power Syst 18(1):69–75

    Article  Google Scholar 

  8. Manoharan H, Srikrishna S, Sivarajan G et al (2018) Economical placement of PMUs considering observability and voltage stability using binary coded ant lion optimization. Int Trans Electr Energy Syst 28(9):e2591

    Article  Google Scholar 

  9. Huang Y, Li S, Li J, et al (2019) A multi-objective model for PMU placement considering redundancy in the presence of line outages. In: 2019 IEEE 3rd conference on energy internet and energy system integration (EI2). IEEE, New York, pp 960–965.

  10. Zhang B (2020) State Estimation and Optimization of Distribution Network Based on PMU. Zhejiang University.

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

    Article  Google Scholar 

  12. Xu B, Abur A (2004) Observability analysis and measurement placement for systems with PMUs. In: IEEE PES power systems conference and exposition, 2004. IEEE, New York, pp 943–946.

  13. Ghosh S, Isbeih YJ, Azman SK, Moursi MSE, El-Saadany E (2022) Optimal PMU allocation strategy for completely observable networks with enhanced transient stability characteristics. IEEE Trans Power Delivery 37(5):4086–4102. https://doi.org/10.1109/TPWRD.2022.3144462

    Article  Google Scholar 

  14. Dubey R, Popov M, Muro JJC (2018) Cost effective wide area measurement systems for smart power network. IEEE Power Energy Technol Syst J 5(3):85–93

    Article  Google Scholar 

  15. Du X (2019) Optimal Placement of Micro-PMU in Distribution System. Southeast University.

  16. Guo X-C, Liao C-S, Chu C-C (2022) Probabilistic optimal PMU placements under limited observability propagations. IEEE Syst J 16(1):767–776. https://doi.org/10.1109/JSYST.2020.3048970

    Article  Google Scholar 

  17. Abdelkader M, Selim A, Kamel S, Jurado F (2019) Optimal placement of phasor measurement units for state estimation of electrical power systems. In: 21st International Middle East Power Systems Conference (MEPCON). IEEE, Cairo, Egypt, pp 1048–1052

  18. 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

    Article  Google Scholar 

  19. Lu C, Wang Z, Ma M, Shen R, Yu Y (2018) An optimal PMU placement with reliable zero injection observation. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2865513

    Article  Google Scholar 

  20. Ahmed MM, Imran K (2019) An optimal PMU placement against N-1 contingency of PMU using integer linear programming approach. In: 2019 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, Taxila, Pakistan, pp 127–132.

  21. Chakrabarti S, Kyriakides E (2008) Optimal placement of phasor measurement units for power system observability. IEEE Trans Power Syst 23(3):1433–1440.s

  22. Almunif A, Fan L (2020) Optimal PMU placement for modeling power grid observability with mathematical programming methods. Int Trans Electr Energy Syst 30(2):e12182

    Article  Google Scholar 

  23. Theodorakatos NP, Manousakis NM, Korres GN (2015) A sequential quadratic programming method for contingency-constrained phasor measurement unit placement. Int Trans Electr Energy Syst 25(12):3185–3211

    Article  Google Scholar 

  24. Kong X, Wang Y, Yuan X et al (2020) Optimal configuration of PMU based on customized genetic algorithm and considering observability of multiple topologies of distribution network. Electric Power Autom Equip 40(01):66–72

    Google Scholar 

  25. Abdelsalam HA, Abdelaziz AY, Osama RA (2014) Impact of distribution system reconfiguration on optimal placement of phasor measurement units. In: Clemson University Power Systems Conference. IEEE, New York, pp 1–6

  26. Bi G (2019) Research on the influence of distributed generation on distribution network. Shandong University

  27. Liu X, **ag Q, Cao Y (2009) Optimal PMU placement to guarantee observability under N−1 condition. Proc CSEE 29(10):47–51

  28. Aminifar F, Fotuhi-Firuzabad M, Safdarian A, Shahidehpour M (2014) Observability of hybrid AC/DC power systems with variable-cost PMUs. IEEE Trans Power Del 29(1):345–352

    Article  Google Scholar 

  29. Arpanahi MK, Alhelou HH, Siano P (2019) A novel multiobjective OPP for power system small signal stability assessment considering WAMS uncertainties. IEEE Trans Industr Inf 16(5):3039–3050

    Article  Google Scholar 

  30. Zhou X, Sun HS, Zhang C et al (2016) Optimal placement of PMUs using adaptive genetic algorithm considering measurementredun-dancy. Int J Reliab Qual Saf Eng 23(3):921–929

    Article  Google Scholar 

  31. Xuebing C, Tengpeng C, King JT, Yuhao S, Amaratunga G (2016) Customized optimal μPMU Placement method for distribution networks. In: IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). **’an, China, pp 135–140. https://doi.org/10.1109/APPEEC.2016.7779485

  32. Kumar VSS, Thukaram D (2015) Approach for multistage placement of phasor measurement units based on stability criteria. IEEE Trans Power Syst 31(4):2714–2725

    Article  Google Scholar 

  33. Abdelsalam HA, Abdelaziz AY, Osama RA, Salem RH (2014) Impact of distribution system reconfiguration on optimal placement of phasor measurement units. In: Clemson University power systems conference. Clemson, SC, USA, pp 1–6. https://doi.org/10.1109/PSC.2014.6808114

  34. Cao P, Liu M (2021) PMU placement method based on improved integer programming method combined with zero-injection nodes. Power System Protec Control 49(16):143–150

    Google Scholar 

  35. Do Coutto Filho MB, de Souza JCS, Tafur JEV (2013) Quantifying observability in state estimation. IEEE Trans Power Syst 28(3):2897–2906.

  36. Wang C, Gao Y (2014) Dynamic reconfiguration of distribution network based on optimal fuzzy c-means clustering and improved chemical reaction optimization. Proc CSEE 34(10):1682–1691

    Google Scholar 

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Acknowledgements

The authors extend their appreciation to the Hunan Electric Power Research Institute for providing data and support to this work.

Funding

This research is funded by [State Grid Hunan Electric Power Company Limited.] under Grant Number [5216A522000M]. The APC is funded by [State Grid Hunan Electric Power Company Limited.] under Grant Number [5216A522000M].

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Authors and Affiliations

  1. State Grid Hunan Electric Power Company Limited Research Institute, Changsha, 410000, China

    Dai Wan, Miao Zhao & Wenhui Mo

  2. State Grid Joint Laboratory for Intelligent Application and Key Equipment in Distribution Network, Changsha, 410000, China

    Dai Wan

  3. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Guidong He, Liang Che & Chunming Tu

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  1. Dai Wan
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  2. Miao Zhao
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  3. Guidong He
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  6. Wenhui Mo
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Corresponding author

Correspondence to Liang Che.

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Appendix A

Appendix A

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Wan, D., Zhao, M., He, G. et al. μPMU Placement Considering the Distribution of Observation Redundancy and Topology Changes in Active Distribution Networks. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01936-2

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  • DOI: https://doi.org/10.1007/s42835-024-01936-2

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