Advertisement

On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification

  • Aleksei Zhukov
  • Nikita Tomin
  • Denis Sidorov
  • Victor Kurbatsky
  • Daniil Panasetsky
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 741)

Abstract

Voltage instability is among the main factors causing large-scale blackouts. One of the major objectives of the Control centers is a prompt assessment of voltage stability and possibly self-healing control of electric power systems. The standing alone solutions based on classical approximation methods are known to be redundant and suffer with limited efficiency. Therefore, the state-of-the-art machine learning algorithms have been adapted for security assessment problem over the last years. This chapter presents an automatic intelligent system for on-line voltage security control based on the Proximity Driven Streaming Random Forest (PDSRF) model using decision trees. The PDSRF combined with capabilities of L-index as a target vector makes it possible to provide the functions of dispatcher warning and “critical” nodes localization. These functions enable self-healing control as part of the security automation systems. The generic classifier processes the voltage stability indices in order to detect dangerous pre-fault states and predict emergency situations. Proposed approach enjoy high efficiency for various scenarios of modified IEEE 118-Bus Test System enabling robust identification of dangerous states.

Notes

Acknowledgements

This is the extended version of the manuscript of the First EAI International Conference on Computer Science and Engineering (COMPSE 2016), November 11–12, 2016, Penang, Malaysia. AZ, DS and DP are partly supported by the International science and technology cooperation program of China and Russia, project 2015DFR70850 and by the National Natural Science Foundation of China Grant No. 61673398.

References

  1. 1.
    Beiraghi, M., & Ranjbar, A. M. (2013). Online voltage security assessment based on wide-area measurements. IEEE Transactions on Power Delivery, 28(1), 989–997. https://doi.org/10.1109/TPWRD.2013.2247426.
  2. 2.
    Diao, R., Sun, K., et al. (2009). Decision tree-based online voltage security assessment using PMU measurements. IEEE Transactions on Power Systems, 24(2), 832–839. https://doi.org/10.1109/TPWRS.2009.2016528.
  3. 3.
    Sidorov, D. (2015). Integral dynamical models: Singularities, signals & control, T. 87. In L. O. Chua (Ed.), World Scientific Series on Nonlinear Science Series A. Singapore: World Scientific Publishing Co Pte Ltd. https://doi.org/10.1142/9789814619196_fmatter.
  4. 4.
    Pao, Y. H., & Sobajic, D. J. (1992). Combined use of unsupervised and supervised learning for dynamic security assessment. IEEE Transactions on Power Systems, 7(3), 878–884. https://doi.org/10.1109/59.141799.
  5. 5.
    Rahimi, F. A., Lauby, M. G., Wrubel, J. N., et al. (1993). Evaluation of the transient energy function method for on-line dynamic security analysis. IEEE Transactions on Power Systems, 8(2), 497–507. https://doi.org/10.1109/59.260834.
  6. 6.
    Tomin, N., Zhukov, A., Sidorov, D., et al. (2015). Random forest based model for preventing large-scale emergencies in power systems. International Journal of Artificial Intelligence, 13(1), 211–228.Google Scholar
  7. 7.
    Wehenkel, L. (1995). Machine learning approaches to power system security assessment. PhD Dissertation, University of Liege.Google Scholar
  8. 8.
    Lachs, W. R. (2002). Controlling grid integrity after power system emergencies. IEEE Transactions on Power Systems, 17(2), 445–450. https://doi.org/10.1109/TPWRS.2002.1007916.
  9. 9.
    Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.
  10. 10.
    Wang, H., Fan, W., et al. (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 226–235).Google Scholar
  11. 11.
    CIGRE Task Force. (2007). Coordinated voltage control in transmission network. C4.602, Technical Report.Google Scholar
  12. 12.
    Alimisis, V., & Taylor, P. C. (2015). Zoning evaluation for improved coordinated automatic voltage control. IEEE Transactions on Power Systems, 30(5), 2736–2746. https://doi.org/10.1109/TPWRS.2014.2369428.
  13. 13.
    Corsi, S. (2000). The secondary voltage regulation in Italy. In 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134), Seattle, WA (Vol. 1, pp. 296-304). https://doi.org/10.1109/PESS.2000.867599.
  14. 14.
    Paul, J. P., Leost, J. Y., & Tesseron, J. M. (1987). Survey of secondary voltage control in France: Present realization and investigation. IEEE Transactions on Power Systems, 2(2), 505–511. https://doi.org/10.1109/TPWRS.1987.4335155.
  15. 15.
    Piret, J. P., Antoine, J. P., Subbe, M., et al. (1992). The study of a centralized voltage control method applicable to the Belgian system. In Proceedings of the CIGRE, Paris, France (pp. 39–201).Google Scholar
  16. 16.
    Sancha, J. L., Fernandez, J. L., Cortes, A., & Abarca, J. T. (1996). Secondary voltage control: Analysis, solutions, simulation results for the Spanish transmission system. IEEE Transactions on Power Systems, 11(2), 630–638. https://doi.org/10.1109/59.496132.
  17. 17.
    Corsi, S., Pozzi, M., et al. (2004). The coordinated automatic voltage control of the Italian transmission grid. Part I: Reasons of the choice and overview of the consolidated hierarchical system. IEEE Transactions on Power Systems, 19(4), 1723–1732. https://doi.org/10.1109/TPWRS.2004.836185.
  18. 18.
    Taylor, C. W. (2006). Discussion of “The coordinated automatic voltage control of the Italian transmission grid”—Part I: Reasons of the choice and overview of the consolidated hierarchical system. IEEE Transactions on Power Systems, 21(1), 444–450. https://doi.org/10.1109/TPWRS.2004.836262.
  19. 19.
    Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655.
  20. 20.
    Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601.
  21. 21.
    Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1.
  22. 22.
    Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1994). Classification and regression trees. New York: Chapman and Hall/CRC.zbMATHGoogle Scholar
  23. 23.
    Kuncheva, L. (2004). Classifier ensembles for changing environment. In F. Roli, J. Kittler, & T. Windeatt (Eds.) 5th International Workshop on Multiple Classifier Systems (pp. 1–15).Google Scholar
  24. 24.
    Zhukov, A. V., Sidorov, D. N., & Foley, A. M. (2017). Random forest based approach for concept drift handling. Communications in Computer and Information Science, 661, 69–77. https://doi.org/10.1007/978-3-319-52920-2_7.
  25. 25.
    Kessel, P., & Glavitsch, H. (1986). Estimating the voltage stability of a power system. IEEE Transactions on Power Delivery, 1(3), 346–353. https://doi.org/10.1109/TPWRD.1986.4308013.
  26. 26.
    Kessel, P., & Glavitsch, H. (1986). Estimating the voltage stability of a power system. IEEE Transactions on Power Delivery, 1(3), 346–354. https://doi.org/10.1109/TPWRD.1986.4308013.
  27. 27.
    Gong, X., Zhang, B., et al. (2014). Research on the method of calculating node injected reactive power based on L indicator. Journal of Power and Energy Engineering, 2, 361–367. https://doi.org/10.4236/jpee.2014.24048.
  28. 28.
    Brzezinski, D. (2010). Mining data streams with concept drift. Dissertation, Poznan University of Technology.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Aleksei Zhukov
    • 1
  • Nikita Tomin
    • 1
    • 2
  • Denis Sidorov
    • 1
    • 2
    • 3
  • Victor Kurbatsky
    • 1
  • Daniil Panasetsky
    • 1
    • 2
  1. 1.ESI SB RASIrkutskRussia
  2. 2.INRTUIrkutskRussia
  3. 3.Hunan UniversityChangshaChina

Personalised recommendations