Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Guimin Xu1This email address is being protected from spambots. You need JavaScript enabled to view it. and Zhengxiang Yang2

1School of Physics and Mechanical & Electrical Engineering, Hubei University of Education, Wuhan 430205, China

2School of intelligent manufacturing, Wuhan Technical College of Communications, Wuhan 430065, China


 

 

Received: June 8, 2023
Accepted: October 26, 2023
Publication Date: April 23, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202502_28(2).0016  


Today’s power systems for various reasons, including linearization of equations of state estimation and improved speed control and protection systems, use Wide Area Measurement (WAMS). Due to their high cost, it is essential to optimize the number and placement of this equipment. The costs associated with deploying a Synchro phasor have evolved throughout time. The cost of upgrading a substation, which is far more than the cost of a single device, has emerged as the major expense component. When determining where to put phasor measuring units (PMUs), it’s important to consider not only how many will need to be built at each substation but also how many will need to undergo upgrades to accommodate them. In this paper, the multi-functional locating of phasor measurement units has been done with goals such as the cost of investment and risk in power systems (reliability) by indicating the observability. Then the location of the phasor units was solved in the form of an optimization problem, using multi-objective optimization algorithm NSGA-II. In the end, the performance of the proposed method is examined on the 9-bus system.


Keywords: Observability of Power Systems; Optimal PMU Allocation; Power System Reliability; Risk Cost; NSGA-II Algorithm


  1. [1] A. P. S. Meliopoulos, G. J. Cokkinides, O. Wasynczuk, E. Coyle, M. Bell, C. Hoffmann, C. Nita-Rotaru, T. Downar, L. Tsoukalas, and R. Gao. “PMU data characterization and application to stability monitoring”. In: IEEE, 2006, 8–pp.
  2. [2] S. Afzal, B. M. Ziapour, A. Shokri, H. Shakibi, and B. Sobhani, (2023) “Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms" Energy 282: 128446.
  3. [3] F. Aminifar, M. Fotuhi-Firuzabad, M. Shahidehpour, and A. Khodaei, (2010) “Probabilistic multistage PMU placement in electric power systems" IEEE Transactions on Power Delivery 26: 841–849.
  4. [4] M. Patel, S. Aivaliotis, and E. Ellen, (2010) “Real-time application of synchrophasors for improving reliability" NERC report, Oct 1: 4.
  5. [5] V. Terzija, G. Valverde, D. Cai, P. Regulski, V. Madani, J. Fitch, S. Skok, M. M. Begovic, and A. Phadke, (2010) “Wide-area monitoring, protection, and control of future electric power networks" Proceedings of the IEEE 99: 80–93.
  6. [6] A. G. Phadke and J. S. Thorp. Synchronized phasor measurements and their applications. 1. Springer, 2008.
  7. [7] A. Khaleghi, M. O. Sadegh, and M. G. Ahsaee, (2018) “Permanent Fault Location in Distribution System Using Phasor Measurement Units (PMU) in Phase Domain." International Journal of Electrical Computer Engineering (2088-8708) 8:
  8. [8] C.-W. Liu and J. Thorp, (1995) “Application of synchronised phasor measurements to real-time transient stability prediction" IEE Proceedings-Generation, Transmission and Distribution 142: 355–360.
  9. [9] T. T. Nguyen and V. L. Nguyen. “Application of widearea network of phasor measurements for secondary voltage control in power systems with FACTS controllers”. In: IEEE, 2005, 2927–2934.
  10. [10] A. G. Phadke, (1993) “Synchronized phasor measurements in power systems" IEEE Computer Applications in power 6: 10–15.
  11. [11] L. Wang, P. P. Gelberger, and N. Ramani. “Reliability assessment of the operational functions of a power system control center”. In: IET, 1991, 229–234.
  12. [12] M. S. Ghazizadeh and M. R. Aghamohammadi, (2023) “A Deep Learning-Based Attack Detection Mechanism against Potential Cascading Failure Induced by Load Redistribution Attacks" IEEE Transactions on Smart Grid:
  13. [13] M. Zima, M. Larsson, P. Korba, C. Rehtanz, and G. Andersson, (2005) “Design aspects for wide-area monitoring and control systems" Proceedings of the IEEE 93: 980–996.
  14. [14] Y. Wang, W. Li, J. Lu, and H. Liu, (2009) “Evaluating multiple reliability indices of regional networks in wide area measurement system" Electric Power Systems Research 79: 1353–1359.
  15. [15] Y. Wang, W. Li, and J. Lu, (2010) “Reliability analysis of wide-area measurement system" IEEE Transactions on Power Delivery 25: 1483–1491.
  16. [16] F. Aminifar, M. Fotuhi-Firuzabad, M. Shahidehpour, and A. Khodaei, (2011) “Observability enhancement by optimal PMU placement considering random power system outages" Energy Systems 2: 45–65.
  17. [17] F. Aminifar, M. Fotuhi-Firuzabad, M. Shahidehpour, and A. Khodaei, (2010) “Probabilistic multistage PMU placement in electric power systems" IEEE Transactions on Power Delivery 26: 841–849.
  18. [18] N. H. Abbasy and H. M. Ismail, (2009) “A unified approach for the optimal PMU location for power system state estimation" IEEE Transactions on power systems 24: 806–813.
  19. [19] F. Aminifar, A. Khodaei, M. Fotuhi-Firuzabad, and M. Shahidehpour, (2009) “Contingency-constrained PMU placement in power networks" IEEE Transactions on Power Systems 25: 516–523.
  20. [20] C. Rakpenthai, S. Premrudeepreechacharn, S. Uatrongjit, and N. R. Watson, (2006) “An optimal PMU placement method against measurement loss and branch outage" IEEE transactions on power delivery 22: 101–107.
  21. [21] S. Chakrabarti and E. Kyriakides, (2008) “Optimal placement of phasor measurement units for power system observability" IEEE Transactions on power systems 23: 1433–1440.
  22. [22] K. Deb. Multi-objective optimisation using evolutionary algorithms: an introduction. Springer, 2011, 3–34.
  23. [23] J. B. A. London, S. A. R. Piereti, R. A. de Souza Benedito, and N. G. Bretas, (2009) “Redundancy and observability analysis of conventional and PMU measurements" IEEE Transactions on Power Systems 24: 1629–1630.
  24. [24] F. Aminifar, C. Lucas, A. Khodaei, and M. FotuhiFiruzabad, (2009) “Optimal placement of phasor measurement units using immunity genetic algorithm" IEEE Transactions on power delivery 24: 1014–1020.
  25. [25] F. Aminifar, M. Fotuhi-Firuzabad, and A. Safdarian, (2012) “Optimal PMU placement based on probabilistic cost/benefit analysis" IEEE Transactions on Power Systems 28: 566–567.
  26. [26] D. Dua, S. Dambhare, R. K. Gajbhiye, and S. A. Soman, (2008) “Optimal multistage scheduling of PMU placement: An ILP approach" IEEE Transactions on Power delivery 23: 1812–1820.
  27. [27] A. Khaleghi, M. O. Sadegh, M. Ghazizadeh-Ahsaee, and A. M. Rabori, (2018) “Transient fault area location and fault classification for distribution systems based on wavelet transform and adaptive neuro-fuzzy inference system (ANFIS)" Advances in Electrical and Electronic Engineering 16: 155–166.


    



 

1.6
2022CiteScore
 
 
60th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.