آمار

تعداد نشریات26
تعداد شماره‌ها386
تعداد مقالات3,394
تعداد مشاهده مقاله5,182,685
تعداد دریافت فایل اصل مقاله3,533,117
Jabari, Farkhondeh, Shabanzadeh, Morteza, Zeraati, Mehdi. (1404). A Novel Meter Placement Algorithm Based on Monte Carlo Coupled State Estimation and Iterative Nonlinear Mesh Adaptive Direct Search. سامانه مدیریت نشریات علمی, 13(3), 184-195. doi: 10.22098/joape.2023.12842.1976
Farkhondeh Jabari; Morteza Shabanzadeh; Mehdi Zeraati. "A Novel Meter Placement Algorithm Based on Monte Carlo Coupled State Estimation and Iterative Nonlinear Mesh Adaptive Direct Search". سامانه مدیریت نشریات علمی, 13, 3, 1404, 184-195. doi: 10.22098/joape.2023.12842.1976
Jabari, Farkhondeh, Shabanzadeh, Morteza, Zeraati, Mehdi. (1404). 'A Novel Meter Placement Algorithm Based on Monte Carlo Coupled State Estimation and Iterative Nonlinear Mesh Adaptive Direct Search', سامانه مدیریت نشریات علمی, 13(3), pp. 184-195. doi: 10.22098/joape.2023.12842.1976
Jabari, Farkhondeh, Shabanzadeh, Morteza, Zeraati, Mehdi. A Novel Meter Placement Algorithm Based on Monte Carlo Coupled State Estimation and Iterative Nonlinear Mesh Adaptive Direct Search. سامانه مدیریت نشریات علمی, 1404; 13(3): 184-195. doi: 10.22098/joape.2023.12842.1976

A Novel Meter Placement Algorithm Based on Monte Carlo Coupled State Estimation and Iterative Nonlinear Mesh Adaptive Direct Search

Journal of Operation and Automation in Power Engineering
مقاله 1، دوره 13، شماره 3، 2025، صفحه 184-195    اصل مقاله (1.06 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.12842.1976
نویسندگان
Farkhondeh Jabari* ؛ Morteza Shabanzadeh؛ Mehdi Zeraati
Power Systems Operation and Planning Research Department, Niroo Research Institute (NRI), Shahrak Ghods, Tehran, Iran.
چکیده
Distribution system state estimation (DSSE) is widely used for real-time monitoring of power grids, where different types of metering devices such as phasor measurement units, smart meters, power quality meters, and etc. are installed. The accuracy of estimated states and the system observability level depends on the type, number and location of meters and since there are many nodes and branches in such large networks, a highly redundant measurement infrastructure is practically unattainable due to the limited investment budget. Hence, this paper proposes a novel meter placement algorithm aiming to minimize the distribution system state estimation error and enhance the system observability level considering the limited number of available meters or investment cost. To this end, on one hand, Monte Carlo simulation (MCS) is applied to a weighted least squares (WLS) based DSSE to find the nodal voltage magnitude and angle as the state variables under the uncertainty of measurements. A MCS and WLS-DSSE hybrid iterative nonlinear optimization mesh adaptive direct search (NOMADS) algorithm is proposed to obtain the best locations of the voltage measuring units considering a trade-off between the DSSE performance and the investment cost. The uncertainties associated with the voltage measurements are modeled using random errors with normal probability distribution function. The efficiency and applicability of the proposed method are analyzed by its implementation on a 25-node unbalanced radial distribution system and numerical results demonstrate that this method technically outperforms other heuristic algorithms in the literature which are usually computationally intractable or more demanding in finding the optimal meter places under uncertainties. Compared to other recently developed algorithms, the accuracy of the estimated states as well as the runtime of the proposed algorithm are improved significantly especially under severe measuring errors. Moreover, it is capable to find the minimum number of voltage meters ensuring that the system observability criterion and the expected DSSE accuracy are fulfilled under the uncertain operating conditions. 
کلیدواژه‌ها
Distribution system state estimation (DSSE)؛ weighted least squares (WLS)؛ meter placement؛ nonlinear optimization mesh adaptive direct search (NOMADS)
مراجع
  1. Jabari, B. Mohammadi Ivatloo, B. Sharifian, and H. Ghaebi, “Day-ahead economic dispatch of coupled desalinated water and power grids with participation of compressed air energy storages,” J. Oper. Autom. Power Eng., vol. 7, no. 1, pp. 40–48, 2019.
  2. Angioni, J. Shang, F. Ponci, and A. Monti, “Real-time monitoring of distribution system based on state estimation,” IEEE Trans. Instrum. Meas., vol. 65, no. 10, pp. 2234–2243, 2016.
  3. Jabari, A. Bahmanyar, and M. Shabanzadeh, “Optimal meter placement in distribution feeders using branch-current based three-phase state estimation: A quest for observability enhancement,” in 10th Smart Grid Conf. (SGC), pp. 1–6, IEEE, 2020.
  4. Amiri and R. Kazemzadeh, “Enhancement of two-step state estimation performance in unbalanced distribution networks,” Comput. Electr. Eng., vol. 86, p. 106724, 2020.
  5. Jabari, M. Zeraati, M. Sheibani, and H. Arasteh, “Robust self-scheduling of pvs-wind-diesel power generation units in a standalone microgrid under uncertain electricity prices,” J. Oper. Autom. Power Eng., vol. 12, no. 2, pp. 152–162, 2024.
  6. N. Korres and N. M. Manousakis, “A state estimator including conventional and synchronized phasor measurements,” Comput. Electr. Eng., vol. 38, no. 2, pp. 294–305, 2012.
  7. Janssen, T. Sezi, and J.-C. Maun, “Meter placement impact on distribution system state estimation,” 2013.
  8. Zeraati, M. Shabanzadeh, M. R. Sheibani, and F. Jabari, “Meter placement algorithms to enhance distribution systems state estimation: Review, challenges and future research directions,” IET Renewable Power Gener., vol. 16, no. 15, pp. 3422–3444, 2022.
  9. Al-Wakeel, J. Wu, and N. Jenkins, “State estimation of medium voltage distribution networks using smart meter measurements,” Appl. Energy, vol. 184, pp. 207–218, 2016.
  10. Singh, B. C. Pal, and R. B. Vinter, “Measurement placement in distribution system state estimation,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 668–675, 2009.
  11. G. Damavandi, V. Krishnamurthy, and J. R. Martí, “Robust meter placement for state estimation in active distribution systems,” IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 1972–1982, 2015.
  12. C. Schweppe and J. Wildes, “Power system static-state estimation, part i: Exact model,” IEEE Trans. Power Appar. Syst., no. 1, pp. 120–125, 1970.
  13. M. Joshi and H. Verma, “Synchrophasor measurement applications and optimal pmu placement: A review,” Electr. Power Syst. Res., vol. 199, p. 107428, 2021.
  14. Nazari-Heris and B. Mohammadi-Ivatloo, “Application of heuristic algorithms to optimal pmu placement in electric power systems: An updated review,” Renewable Sustainable Energy Rev., vol. 50, pp. 214–228, 2015.
  15. Aziz, S. Khalid, M. Mustafa, H. Shareef, and G. Aliyu, “Artificial intelligent meter development based on advanced metering infrastructure technology,” Renewable Sustainable Energy Rev., vol. 27, pp. 191–197, 2013.
  16. Jabari, B. Mohammadi Ivatloo, B. Sharifian, and H. Ghaebi, “Optimal short-term coordination of desalination, hydro and thermal units,” J. Oper. Autom. Power Eng., vol. 7, no. 2, pp. 141–147, 2019.
  17. Chen, J. Lin, C. Wan, Y. Song, S. You, Y. Zong, W. Guo, and Y. Li, “Optimal meter placement for distribution network state estimation: A circuit representation based milp approach,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 4357–4370, 2016.
  18. M. Ferreira, P. M. Carvalho, and L. A. Ferreira, “Optimal meter placement in low observability distribution networks with der,” Electr. Power Syst. Res., vol. 189, p. 106707, 2020.
  19. Khanjani and S. M. Moghaddas-Tafreshi, “An ilp model for stochastic placement of µpmus with limited voltage and current channels in a reconfigurable distribution network,” Int. J. Electr. Power Energy Syst., vol. 148, p. 108951, 2023.
  20. C. Tapia and A. R. Messina, “Constrained sensor placement and state reconstruction in power systems from partial system observations,” Int. J. Electr. Power Energy Syst., vol. 146, p. 108720, 2023.
  21. Veerakumar, D. Cetenovi´ c, K. Kongurai, M. Popov,´ A. Jongepier, and V. Terzija, “Pmu-based real-time distribution system state estimation considering anomaly detection, discrimination and identification,” Int. J. Electr. Power Energy Syst., vol. 148, p. 108916, 2023.
  22. Hassannejad Marzouni, A. Zakariazadeh, and P. Siano, “Measurement devices allocation in distribution system using state estimation: A multi-objective approach,” Int. Trans. Electr. Energy Syst., vol. 30, no. 8, p. e12469, 2020.
  23. Liu, F. Ponci, A. Monti, C. Muscas, P. A. Pegoraro, and S. Sulis, “Optimal meter placement for robust measurement systems in active distribution grids,” IEEE Trans. Instrum. Meas., vol. 63, no. 5, pp. 1096–1105, 2014.
  24. Prasad and D. M. Vinod Kumar, “Robust meter placement for active distribution state estimation using a new multiobjective optimisation model,” IET Sci. Meas. Technol., vol. 12, no. 8, pp. 1047–1057, 2018.
  25. Rosli, H. Mokhlis, K. Naidu, J. Jamian, and A. H. A. Bakar, “Improving state estimation accuracy through incremental meter placement using new evolutionary strategy,” Arabian J. Sci. Eng., vol. 39, pp. 7981–7989, 2014.
  26. Wang, W. Zhang, and Y. Liu, “A robust measurement placement method for active distribution system state estimation considering network reconfiguration,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 2108–2117, 2016.
  27. Prasad and V. K. Dulla Mallesham, “Multi-objective hybrid estimation of distribution algorithm-interior point method-based meter placement for active distribution state estimation,” IET Gener. Trans. Distrib., vol. 12, no. 3, pp. 767–779, 2018.
  28. A. Raposo, A. B. Rodrigues, and M. d. G. da Silva, “Optimal meter placement algorithm for state estimation in power distribution networks,” Electr. Power Syst. Res., vol. 147, pp. 22–30, 2017.
  29. Yao, X. Liu, and Z. Li, “Robust measurement placement for distribution system state estimation,” IEEE Trans. Sustainable Energy, vol. 10, no. 1, pp. 364–374, 2017.
  30. M. Othman, M. Ahmed, and M. Salama, “A novel smart meter technique for voltage and current estimation in active distribution networks,” Int. J. Electr. Power Energy Syst., vol. 104, pp. 301–310, 2019.
  31. Ketabi, M. R. Sheibani, and S. M. Nosratabadi, “Power quality meters placement using seeker optimization algorithm for harmonic state estimation,” Int. J. Electr. Power Energy Syst., vol. 43, no. 1, pp. 141–149, 2012.
  32. Ibarra, J. Avilés, D. Guillen, J. Mayo-Maldonado, J. ValdezResendiz, and P. Ponce, “Optimal micro-pmu placement and virtualization for distribution network changing topologies,” Sustainable Energy Grids Networks, vol. 27, p. 100510, 2021.
  33. S. Tabar, S. Tohidi, S. Ghassemzadeh, and P. Siano, “Enhancing security and observability of distribution systems with optimal placement of µpmus and firewalls,” Int. J. Electr. Power Energy Syst., vol. 135, p. 107601, 2022.
  34. Rabiee, “An advanced state estimation method using virtual meters,” J. Oper. Autom. Power Eng., vol. 5, no. 1, pp. 11–18, 2017.
  35. Mazlumi, M. Mohammad Gholiha, R. Abbasi, and F. Mazlumi, “Presenting a new method based on branch placement for optimal placement of phasor measurement units,” J. Oper. Autom. Power Eng., vol. 2, no. 1, pp. 32–39, 2014.
  36. Eghbali, R. Kazemzadeh, and K. Amiri, “Multi-area state estimation based on pmu measurements in distribution networks,” J. Oper. Autom. Power Eng., vol. 8, no. 1, pp. 65–74, 2020.
  37. Audet, V. Béchard, and S. L. Digabel, “Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search,” J. Global Optim., vol. 41, pp. 299–318, 2008.
  38. A. Taher and R. Bagherpour, “A new approach for optimal capacitor placement and sizing in unbalanced distorted distribution systems using hybrid honey bee colony algorithm,” Int. J. Electr. Power Energy Syst., vol. 49, pp. 430–448, 2013.
  39. Audet, G. Couture, and J. Dennis Jr, “Nomad project (ltmads package),” Software Available www. gerad. ca/nomad.
  40. E. L. Lawler and D. E. Wood, “Branch-and-bound methods: A survey,” Oper. Res., vol. 14, no. 4, pp. 699–719, 1966.
  41. Lofberg, “Yalmip: A toolbox for modeling and optimization in matlab,” in 2004 IEEE Int. Conf. Rob. Autom. (IEEE Cat. No. 04CH37508), pp. 284–289, IEEE, 2004.
  42. Singh, B. C. Pal, R. A. Jabr, and R. B. Vinter, “Meter placement for distribution system state estimation: An ordinal optimization approach,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2328–2335, 2011.
  43. A. Raposo, A. B. Rodrigues, and M. d. G. da Silva, “Robust meter placement for state estimation considering distribution network reconfiguration for annual energy loss reduction,” Electr. Power Syst. Res., vol. 182, p. 106233, 2020.
آمار
تعداد مشاهده مقاله: 503
تعداد دریافت فایل اصل مقاله: 513


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب