A response-based approach to online prediction of generating unit angular stability

Document Type : Research Article

Authors

Centre of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran.

Abstract

In this paper, first, a rotor angle trajectory model based on polynomial functions is proposed. Afterwards, a response-based approach for online prediction of power system angular instability is presented. The proposed method utilizes bus phase angle data measured by phasor measurement unit at the point of common coupling of power plant transformer to the bulk power grid. In the prediction process, by computing the second order derivative of post-fault data, the starting point of the calculation data window is determined. Next, a fifth-degree polynomial curve is fitted on the designated data window to predict the angular curve of generating unit. Based on the sign of the first order derivative of predicted curve, the angular stability of generating unit is judged. This approach is testified on the western system coordinating council standard test bed under different operation and fault type scenarios. Taking into account various fault conditions and their associated occurrence probability, a probabilistic index is also defined to sum up the overall performance of the new method. Simulation results confirm that the proposed method outperforms the existing ones in terms of both accuracy and speed. Prediction results could be used in generator rejection schemes to prevent severe power plant outages.

Keywords

References

References:
1. Khandani, A. and Akbari Foroud, A. "Providing transient stability by excitation system response improvement methods through long-term contracts", Scientia Iranica, 26(3), pp. 1652-1663 (2019).
2. Aminifar, F., Fotuhi-Firuzabad, M., Safdarian, A., et al. "Synchrophasor measurement technology in power systems: panorama and state-of-the-art", IEEE Access, 2, pp. 1607-1628 (2014). 
3. Kundur, P., Power System Stability & Control, Tata McGraw-Hill, Ed., 5th Reprint, New Delhi (2008).
4. Chih-Wen, L., Mu-chun, S., Shuenn-Shing, T., et al. "Application of a novel fuzzy neural network to realtime transient stability swings prediction based on synchronized phasor measurements", IEEE Transactions on Power Systems, 14, pp. 685-692 (1999). 
5. Xiaochen, W., Jinquan, Z., Aidong, X., et al. "Review on transient stability prediction methods based on real time wide-area phasor measurements", 4th Int. Conf. on Electric Utility Deregulation and Restructuring and Power Technologies., pp. 320-326 (2011).
6. Karady, G.G. and Jun, G. "A hybrid method for generator tripping", IEEE Transactions on Power Systems, 17, pp. 1102-1107 (2002).
7. Hazra, J., Reddi, R.K., Das, K., et al. "Power grid transient stability prediction using wide area synchrophasor measurements", 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), pp. 1-8 (2012).
8. Esmaili, M., Hajnoroozi, A.A., and Shayanfar, H.A. "Risk evaluation of online special protection systems", International Journal of Electrical Power & Energy Systems, 41, pp. 137-144 (2012).
9. Kun, M., Xu, P., Zhao, J., et al. "Comparison of methods for the perturbed trajectory prediction based on wide area measurements", IEEE Power Engineering and Automation Conference (PEAM), pp. 321-325 (2011).
10. Liu, C.W. and Thorp, J. "Application of synchronised phasor measurements to real-time transient stability prediction", IEE Proceedings - Generation, Transmission and Distribution, 142, pp. 355-360 (1995).
11. Hashiesh, F., Mostafa, H.E., Khatib, A.R., et al. "An Intelligent wide area synchrophasor based system for predicting and mitigating transient instabilities", IEEE Transactions on Smart Grid, 3, pp. 645-652 (2012).
12. Siddiqui, S.A., Verma, K., Niazi, K.R., et al. "Realtime monitoring of post-fault scenario for determining generator coherency and transient stability through ANN", IEEE Transactions on Industry Applications, 54(1), pp. 685-692 (2018).
13. Zhou, Y., Wu, J., Hao, L., et al. "Transient stability prediction of power systems using post-disturbance rotor angle trajectory cluster features", Electric Power Components and Systems, 44(17), pp. 1879-1891 (2016).
14. Tingyan, G. and Milanovi, J.V. "The effect of quality and availability of measurement signals on accuracy of on-line prediction of transient stability using decision tree method", IEEE PES ISGT Europe, pp. 1-5 (2013).
15. Guo, T. and Milanovi, J.V. "On-line prediction of transient stability using decision tree method; sensitivity of accuracy of prediction to different uncertainties", IEEE Conf. PowerTech. Grenoble, pp. 1-6 (2013).
16. Sun, K., Lee, S.T., and Zhang, P. "An adaptive power system equivalent for real-time estimation of stability margin using -plane trajectories", IEEE Transactions on Power Systems, 26, pp. 915-923 (2011).
17. Bretas, N.G. and Phadke, A.G. " Real time instability prediction through adaptive time series coefficients", IEEE Power Engineering Society, Winter Meeting, pp. 731-736 (1999).
18. Alinezhad, B. and Karegar, H.K. "Predictive out-ofstep relay based on equal area criterion and PMU data", Int Trans Electr Energ Syst, 27:e2327 (2017).
19. Zare, H., Alinejad, B.Y., and Yaghobi, H. "Intelligent prediction of out-of-step condition on synchronous generators because of transient instability crisis", Int Trans Electr Energ Syst., 29:e2686 (2019).
20. Sobbouhi, A.R. and Aghamohammadi, M.R. "A new algorithm for predicting out-of-step using rotor speed-acceleration based on phasor measurement units (PMU) Data", Electric Power Components and Systems, 43, pp. 1478-1486 (2015).
21. Diaz-Alzate, A.F., Candelo-Becerra, J., and Villa Sierra, J. "Transient stability prediction for realtime operation by monitoring the relative angle with predefined thresholds", Energies MDPI, Open Access Journal, EE 423, 12(5), pp. 1-17 (March 2019).
22. Bhui, P. and Senroy, N. "Real-time prediction and control of transient stability using transient energy function", IEEE Transactions on Power Systems, 32(2), pp. 923-934 (2017).
23. Zima, M. "Special protection schemes in electric power systems", EEH-Power Systems Laboratory, pp. 1-22 (2002).
24. Guo, H., Liu, C.C., and Wang, G. "Lyapunov exponents over variable window sizes for prediction of rotor angle stability", North American Power Symposium, pp. 1-6 (2014).
25. Weihui, F., Sanyi, Z., McCalley, J.D., et al. "Risk assessment for special protection system", IEEE Transactions on Power Systems, 17, pp. 63-72 (2002).
26. Rahimi Pordanjani, I., Askarian Abyaneh, H., Sadeghi, S.H.H., et al. "Risk reduction in special protection systems by using an online method for transient instability prediction", International Journal of Electrical Power & Energy Systems, 32, pp. 156-162 (2010).
27. Weckesser, T., Johannsson, H., and stergaard, J. "Impact of model detail of synchronous machines on real-time transient stability assessment", IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid, Rethymno, pp. 1-9 (2013).
28. Teimourzadeh, S., Davarpanah, M., Aminifar, F., et al. "An adaptive auto-reclosing scheme to preserve transient stability of microgrids", IEEE Transactions on Smart Grid, 9(4), pp. 2638-2646 (2016).
29. Hajnoroozi, A.A., Aminifar, F., and Ayoubzadeh, H. "Generating unit model validation and calibration through synchrophasor measurements", IEEE Transactions on Smart Grid, 6, pp. 441-449 (2015).
30. Gopakumar, P., Reddy, M.J.B., and Mohanta, D.K. "Transmission line fault detection and localisation methodology using PMU measurements", IET Generation, Transmission & Distribution, 9, pp. 1033-1042 (2015).
31. IEEE standard for synchrophasor measurements for power systems. IEEE Std C37.118.1-2011 (Revision of IEEE Std C37.118-2005), pp. 1-61 (2011).
32. DIgSILENT PowerFactory, available at:/http://www. digsilent.de/.
33. Lucas, J.R. "Power system analysis: faults", EE 423 (2005).
Scientia Iranica
Volume 26, Special Issue on machine learning, data analytics, and advanced optimization techniques...
Transactions on Computer Science & Engineering and Electrical Engineering (D)
November and December 2019
Pages 3592-3605

Files

History

  • Receive Date: 14 July 2019
  • Accept Date: 07 October 2019

Share

How to cite

Statistics