Synchronized Measurements based Online Transient Stability Assessment using Gaussian Process Regression

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P. K. Chandrashekhar
S. G. Srivani

Abstract

In this paper an online post-fault Transient Stability Assessment (TSA) method using synchronized measurements or PMU measurements and Gaussian Process Regression (GPR) is presented. A post-fault multi-machine system is converted into two machine groups (Critical and Non critical) then into a suitable OMIB system using Single Machine Equivalent (SIME) method. With the help of thus obtained OMIB Pa-δ trajectory, a normalized Transient Stability Margin (TSM) is proposed offline. By using pre and during fault synchronized measurements as input, different GPR models are trained offline to predict the normalized stability margin. Keeping RMSE as a measure, a best suitable model is chosen for prediction. After a fault, the synchronized measurements are used as input to this trained model to predict the stability margin online. If the predicted margin is negative, then the post-fault system said to be unstable. If the predicted margin is positive, then the system is stable. The proposed assessment method is tested using New England 39 bus test system. The results are compared with offline simulations. High prediction accuracy rates are observed for GPR models, making them suitable for online TSA.

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How to Cite
Chandrashekhar, P. K., & Srivani, S. G. (2020). Synchronized Measurements based Online Transient Stability Assessment using Gaussian Process Regression. Power Research - A Journal of CPRI, 129–135. https://doi.org/10.33686/pwj.v16i2.158039

References

  1. IEEE/CIGRE Joint Task Force. Definition and classificationof power system stability. IEEE Trans Power System. 2004;1(2).
  2. Kundur P. Power system stability and control. McGraw-Hill; 1994.
  3. Sauer PW, Pai MA. Power system dynamics and stability. New Jersey: Prentice-Hall; 1998.
  4. Pai MA. Energy function analysis for power system stability. Kluwer Academic; 1989. https://doi.org/10.1007/978-1-4613-1635-0
  5. Fouad AA, Vittal V. Power system transient stability analysis using the transient energy function method. Prentice-Hall
  6. https://doi.org/10.1109/MPER.1991.88730
  7. Xue, Yusheng, Van Custem T, Ribbens-Pavella M. Extended equal area criterion justifications, generalizations, applications.
  8. IEEE Trans on Power Systems. 1989; 4(1):44–52. https://doi.org/10.1109/59.32456
  9. Pavella M, Ernst D, Ruiz-Vega D. Transient stability of power systems: A unified approach to assessment and control. Springer; 2000. https://doi.org/10.1007/978-1-4615-4319-0
  10. Wadduwage, Prasad D, Annakkage UD, Wu CQ. Hybrid algorithm for rotor angle security assessment in power systems.
  11. The Journal of Engineering. 2015; 8(2015):241–51. https://doi.org/10.1049/joe.2015.0033
  12. Zhang Y, Wehenkel L, Rousseaux P, Pavella M. SIME: A hybrid approach to fast transient stability assessment and contingency selection. International Journal of Electrical Power & Energy Systems.1997; 19(3):195–208. https://doi.org/10.1016/S0142-0615(96)00047-6
  13. Fang DZ, Chung TS, David AK. Improved techniques for hybrid method in fast-transient stability assessment. IEE Proceedings-Generation, Transmission and Distribution. 1997; 144(2):107212. https://doi.org/10.1049/ip-gtd:19970889
  14. Fang DZ, David AK, Kai C, Yunli C. Improved hybrid approach to transient stability assessment. IEE Proceedings-Generation, Transmission and Distribution. 2005; 152(2):201–7. https://doi.org/10.1049/ip-gtd:20041223
  15. Maria GA, Tang C, Kim J. Hybrid transient stability analysis (power systems). IEEE Trans on Power Systems. 1990; 5(2):384–93. https://doi.org/10.1109/59.54544
  16. Yang H, Zhang W, Shi F, Xie J, Ju W. PMU-based model-free method for transient instability prediction and emergency generator-shedding control. International Journal of Electrical Power & Energy Systems. 2019; 105:381–93. https://doi.org/10.1016/j.ijepes.2018.08.031
  17. Kezunovic M, Meliopoulos S, Venkatasubramanian V, Vittal V. Application of time-synchronized measurements in power system transmission networks. Springer; 2014. https://doi.org/10.1007/978-3-319-06218-1
  18. Bhui P, Senroy N. Real-time prediction and control of transient stability using transient energy function. IEEE Trans on Power Systems 2016; 32(2):923–34. https://doi. org/10.1109/TPWRS.2016.2564444
  19. Guo T, Milanović JV. Online identification of power system dynamic signature using PMU measurements and data mining. IEEE Trans on Power Systems. 2015; 31(3):1760–8. https://doi.org/10.1109/TPWRS.2015.2453424
  20. He M, Vittal V, Zhang J. Online dynamic security assessment with missing PMU measurements: A data mining approach. IEEE Trans on Power Systems. 2013; 28(2):1969– 77. https://doi.org/10.1109/TPWRS.2013.2246822
  21. Hazra J, Reddi RK, Das K, Seetharam DP, Sinha AK. Power grid transient stability prediction using wide area synchrophasor measurements. 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). IEEE; 2012. https://doi.org/10.1109/ISGTEurope.2012.6465752.
  22. PMid:23603567
  23. Zheng C, Malbasa V, Kezunovic M. Regression tree for stability margin prediction using synchrophasor measurements. IEEE Trans on Power Systems. 2012; 28(2):1978–87. https://doi.org/10.1109/TPWRS.2012.2220988
  24. Sun K, Likhate S, Vittal V, Kolluri VS, Mandal S. An online dynamic security assessment scheme using phasor measurements and decision trees. IEEE Trans On Power Systems. 2007; 22(4):1935–43. https://doi.org/10.1109/ TPWRS.2007.908476
  25. Shi Z, Yao W, Zeng L, Wen J, Fang J, Ai X, Wen J. Convolutional neural network-based power system transient stability assessment and instability mode prediction. Applied Energy. 2020; 263. https://doi.org/10.1016/j.apenergy. 2020.114586
  26. Azman SK, Isbeih YJ, El Moursi MS, Elbassioni K. A unified online deep learning prediction model for small signal and transient stability. IEEE Trans on Power Systems. 2020. https://doi.org/10.1109/TPWRS.2020.2999102
  27. Sobbouhi, AR, Vahedi A. Online synchronous generator out-of-step prediction by ellipse fitting on acceleration power - Speed deviation curve. International Journal of Electrical Power & Energy Systems. 2020; 119. https://doi. org/10.1016/j.ijepes.2020.105965
  28. Ma S, Chen C, Liu C, Shen Z. A measurement-simulation hybrid method for transient stability assessment and control based on the deviation energy. International Journal of Electrical Power & Energy Systems. 2020; 115. https://doi.org/10.1016/j.ijepes.2019.105422
  29. Iravani A, De Leon F. Real-time transient stability assessment using dynamic equivalents and nonlinear observers. IEEE Trans on Power Systems. 2020. https://doi.org/10.1109/TPWRS.2020.2968293
  30. Zimmerman RD, Murillo-Sanchez CE. MATPOWER [Software] [Internet]. 2015 Available from: https://matpower.org
  31. MatDyn website [Internet]. Available from: http://www.esat.kuleuven.be/electa/teaching/ matdyn/
  32. MathWorks: Statistics and machine learning toolbox: user’s guide; 2019.