The US power industry has been pushing forward the adoption of synchrophasor technology for wide-area monitoring and situational awareness. Many applications have been developed to take advantage of the GPS time-stamped synchrophasor data. Linear state estimator (LSE) is one of the recent developments in the synchrophasor industry that has been gradually accepted and adopted by several US utilities under pilot projects. This study is aimed to provide the insights of successful deployments at the utility level, and to present key applications and business values of using LSE. One major contribution of this study is that the LSE has been enhanced to a production-grade application for real-time operation at control centre. The other major contribution of this study is to demonstrate the benefits and use cases of the LSE application based on first-hand implementation and deployment experience at utilities. The LSE can (i) validate and condition phasor measurement unit data, (ii) provide an independent non-iterative state estimator (SE) to complement the SE in Energy Management System for situational awareness, data analytics and grid resiliency and (iii) expand synchrophasor measurement observability for down-stream synchrophasor applications. Several use cases are demonstrated in real time by pilot projects deployed at Bonneville Power Administration, Duke Energy, and Southern California Edison. LSE application's use cases and business values are presented to illustrate its successful deployment and operational experience in wide-area monitoring and situational awareness system.

References

1. 1)
  - 15. Jones, K.D., Thorp, J.S., Gardner, R.M.: ‘Three-phase linear state estimation using phasor measurements’. Proc. IEEE Power Engineering Society General Meeting, Vancouver, BC, Canada, July 21–25, 2013, pp. 1–5.
2. 2)
  - 19. Choi, S., Kim, B., Cokkinides, G., et al: ‘Feasibility study: autonomous state estimation in distribution systems’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 2109–2117.
3. 3)
  - 16. Ghiocel, S.G., Chow, J.H., Stefopoulos, G., et al: ‘Phasor-measurement-based state estimation for synchrophasor data quality improvement and power transfer interface monitoring’, IEEE Trans. Power Syst., 2014, 29, (2), pp. 881–888.
4. 4)
  - 7. Kirihara, K., Reinhard, K., Yoon, A., et al: ‘Investigating synchrophasor data quality issues’. Proc. Power and Energy Conf. at Illinois, Champaign, IL, USA, 28 February–1 March 2014, pp. 1–4.
5. 5)
  - 23. Zhang, L.: ‘Validation, testing and implementation of the linear state estimator in a real power system’. PhD thesis, Washington State University, 2014.
6. 6)
  - 11. Yang, T., Sun, H., Bose, A.: ‘Transition to a two-level linear state estimator – Part II: Algorithm’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 54–62.
7. 7)
  - 5. Chen, H., Mo, J., Kothapa, U., et al: ‘Development of an on-line synchrophasor wide-area dynamics monitoring platform’. IEEE PES Transmission & Distribution Conf. & Exposition, Chicago, IL, USA, April 2014.
8. 8)
  - 20. Phadke, A.G., Thorp, J.S., Nuqui, R.F., et al: ‘Recent developments in state estimation with phasor measurements’. IEEE PES Power System Conf. and Exposition, Seattle, USA, March 2009.
9. 9)
  - 1. Phadke, A.G., Thorp, J.S.: ‘Synchronized phasor measurements and their applications’ (Springer, 2008, 1st edn.).
10. 10)
  - 13. Zhang, L., Chen, H., Martin, K.E., et al: ‘Practical issues of implementation of linear state estimator in WECC’. IEEE PES Innovative Smart Grid Technologies Conf. (ISGT), Minneapolis, MN, USA, September 2016.
11. 11)
  - 18. Meliopoulos, A.P.S., Cokkinides, G.J., Galvan, F., et al: ‘Delivering accurate and timely data to all’, IEEE Power Energy Mag., 2007, 5, (3), pp. 74–86.
12. 12)
  - 14. Jones, K.D., Pal, A., Thorp, J.S.: ‘Methodology for performing synchrophasor data conditioning and validation’, IEEE Trans. Power Syst., 2015, 30, (3), pp. 1121–1130.
13. 13)
  - 4. Salfinger, A., Retschitzegger, W., Schwinger, W.: ‘Maintaining situation awareness over time – a survey on the evolution support of situation awareness systems’. Conf. on Technologies and Applications of Artificial Intelligence, Taipei, December 2013, pp. 274–281.
14. 14)
  - 22. Cheng, Y., Lu, C., Men, K., et al: ‘Application of the complex algorithm in PMU-only state estimation’. Power System Technology (POWERCON) Int. Conf., Chengdu, China, December 2014, pp. 349–354.
15. 15)
  - 21. Cheng, Y., Lu, C., Men, K., et al: ‘Research on perception of power system state based on WAMS’. IEEE PES Innovative Smart Grid Technologies Conf. (ISGT), Washington, DC, USA, June 2015.
16. 16)
  - 3. Alcaraz, C., Lopez, J., James, C.: ‘Diagnosis mechanism for accurate monitoring in critical infrastructure protection’, Comput. Stand. Interfaces, 2014, 36, (3), pp. 501–512.
17. 17)
  - 2. Begovic, M., Messina, A.: ‘Editorial: wide-area monitoring, protection and control’, IET. Gener. Transm. Distrib., 2010, 4, (10), pp. 1083–1085.
18. 18)
  - 8. Zhang, Q., Luo, X., Bertagnolli, D., et al: ‘PMU data validation at ISO New England’. Proc. IEEE Power and Energy Society General Meeting, Vancouver, BC, Canada, July 2013, pp. 1–5.
19. 19)
  - 24. Grainger, J.J., Stevenson, W.D.: ‘Power system analysis’ (McGraw-Hill, 2003, 1st edn.).
20. 20)
  - 25. Shelton, M.L., Mittelstadt, W.A., Winkelmen, P.F., et al: ‘Bonneville power administration 1400 MW braking resistor’, Trans. IEEE, 1975, 94, (2), pp. 602–611.
21. 21)
  - 12. Zhang, L., Bose, A., Jampala, A., et al: ‘Design, testing, and implementation of a linear state estimator in a real power system’, IEEE Trans. Smart Grid, PP, (99), pp. 1–8, doi: 10.1109/TSG.2015.2508283.
22. 22)
  - 27. ‘A real-world implementation of centralized RAS system’, https://www.pacw.org/issue/march_2014_issue/network_architecture/scalable_network_architecture_based_on_ip_multicast_for_synchrophasor_applications.html, accessed 09 April2014.
23. 23)
  - 9. Abur, A., Exposito, A.G.: ‘Power system state estimation: theory and implementation’ (Marcel Dekker, 2004, 1st edn.).
24. 24)
  - 26. Litzenberger, W.: ‘A short history of the pacific HVDC Intertie’. IEEE Power Systems Conf. and Exposition, Bryan, Texas, March 2006, pp. 24–27.
25. 25)
  - 6. Chen, H., Zhang, L., Mo, J., et al: ‘Synchrophasor-based real-time state estimation and situational awareness system for power system operation’, J. Modern Power Syst. Clean Energy, 2016, 4, (3), pp. 370–382.
26. 26)
  - 10. Yang, T., Sun, H., Bose, A.: ‘Transition to a two-level linear state estimator – Part I: Architecture’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 46–53.
27. 27)
  - 17. Vanfretti, L., Chow, J.H., Sarawgi, S., et al: ‘A phasor-data-based state estimator incorporating phase bias correction’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 111–119.

Successful deployment and operational experience of using linear state estimator in wide-area monitoring and situational awareness projects

References

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