Automation and Remote Control

, Volume 79, Issue 10, pp 1741–1755 | Cite as

A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections

  • N. I. VoropaiEmail author
  • N. V. Tomin
  • D. N. Sidorov
  • V. G. Kurbatsky
  • D. A. Panasetsky
  • A. V. Zhukov
  • D. N. Efimov
  • A. B. Osak
Control Problems for the Development of Large-Scale Systems


We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random forest algorithm. The suite allows to recognize dangerous modes of complex closed-loop EPS, preventing the risk of emergencies on early stages. We show results of experimental tests on IEEE test systems.


agent modeling machine learning emergency automatics electric power systems voltage collapse L-index 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Voropai, N.I., Tomin, N.V., Kurbatskii, V.G., et al., Kompleks intellektual’nykh sredstv dlya predotvrashcheniya krupnykh avarii v elektroenergeticheskikh sistemakh (A Suite of Intelligent Means of Prevention Large-Scale Failures in Electric Power Systems), Novosibirsk: Nauka, 2016.Google Scholar
  2. 2.
    Voropai, N.I. and Saratova, N.E., Analyzing the Statistics of RZA Failures Based on Microprocessors from the Point of View of Accounting for them in Modeling Cascade Failures, Probl. Energetiki, 2008, no. 11/12 (1), pp. 66–71.Google Scholar
  3. 3.
    Voropai, N.I., Snizhenie riskov kaskadnykh avarii v elektroenergeticheskikh sistemakh (Reducing the Risks of Cascade Failures in Electric Power Systems), Novosibirsk: Sib. Otd. Ross. Akad. Nauk, 2011.Google Scholar
  4. 4.
    IEEE PES CAMS Task Force on Understanding, Prediction, Mitigation and Restoration of Cascading Failures, “Initial Review of Methods for Cascading Failure Analysis in Electric Power Transmission Systems,” Proc. IEEE PES General Meeting, Pittsburgh, July, 2008.Google Scholar
  5. 5.
    Negnevitsky, M., An Expert System Application for Clearing Overloads, Int. J. Power Energy Syst., 1995, vol. 15, no. 1, pp. 9–13.Google Scholar
  6. 6.
    Barkans, E. and Zhalostiba, D., Zashchita ot razvalov i samovosstanovlenie energosistem (Protection against Critical Failures and Self-Restoration of Power Systems), Cheboksary: RITs “SRZAU,” 2014.Google Scholar
  7. 7.
    Kessel, P. and Glavitsch, H., Estimating the Voltage Stability of a Power System, IEEE Trans. Power Delivery, 1986, vol. 1, no. 3, pp. 346–354.CrossRefGoogle Scholar
  8. 8.
    Voitov, O.N., Voropai, N.I., Gamm, A.Z., et al., Analiz neodnorodnostei elektroenergeticheskikh sistem (Analysis of Nonuniformities in Electric Power Systems), Novosibirsk: Nauka, 1999.Google Scholar
  9. 9.
    Goh, H., Comparative Study of Different Kalman Filter Implementations in Power System Stability, Am. J. Appl. Sci., 2014, vol. 11, no. 8, pp. 1379–1390.CrossRefGoogle Scholar
  10. 10.
    Karbalaei, F., Soleymani, H., and Afsharnia, S., A Comparison of Voltage Collapse Proximity Indicators, IPEC, 2010 Conf. Proc., 2010, pp. 429–432.CrossRefGoogle Scholar
  11. 11.
    Sayed Shah, D.M., Voltage Stability in Electric Power System: A Practical Introduction, Berlin: Logos Verlag, 2015.Google Scholar
  12. 12.
    Kurbatsky, V.G., Sidorov, D.N., Spiryaev, V.A., and Tomin, N.V., The Hybrid Model Based on Hilbert-Huang Transform and Neural Networks for Forecasting of Short-Term Operation Conditions of Power Systems, Proc. IEEE PES Trondheim PowerTech, Trondheim, 2011, pp. 1–7.Google Scholar
  13. 13.
    Zhukov, A., Tomin, N., Sidorov, D., Panasetsky, D., and Spirayev, V., A Hybrid Artificial Neural Network for Voltage Security Evaluation in a Power System, Proc. 2015 Int. Youth Con. Energy (IYCE), Pisa, 2015, pp. 1–8.Google Scholar
  14. 14.
    Kurbatsky, V., Tomin, N., Sidorov, D., and Spiryaev, V., Application of Two Stages Adaptive Neural Network Approach for Short-Term Forecast of Electric Power Systems, Proc. 10 Int. Conf. Environ. Electr. Engineer., Rome, 2011, pp. 1–4.Google Scholar
  15. 15.
    Manov, N.S., Khokhlov, M.V., Chukreev, Yu.Ya., et al., Metody i modeli issledovaniya nadezhnosti elektroenergeticheskikh sistem (Methods and Models for Reliability Studies of Electric Power Systems), Syktyvkar: Komi Nauchn. Tsentr, Ural. Otd. Ross. Akad. Nauk, 2010.Google Scholar
  16. 16.
    Kurbatskii, V.G., Sidorov, D.N., Spiryaev, V.A., and Tomin, N.V., On the Neural Network Approach for Forecasting of Nonstationary Teme Series on the Basis of the Hilbert–Huang Transform, Autom. Remote Control, 2011, vol. 72, no. 7, pp. 1405–1414.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kalyani, S. and Shanti Swarup, K., Design of Pattern Recognition System for Static Security Assessment and Classification, Patt. Anal. Appl., 2012, vol. 15, pp. 299–311.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jothinathan, K. and Ganapathy, S., Transient Security Assessment in Power Systems Using Deep Neural Network, Int. J. Appl. Engin. Res., 2012, vol. 10, no. 15, pp. 787–790.Google Scholar
  19. 19.
    Diao, R., Sun, K., Vittal, V., et al., Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements, IEEE Trans. Power Syst., 2009, vol. 24, no. 2, pp. 832–839.CrossRefGoogle Scholar
  20. 20.
    Arkhipov, I.L., Ivanov, A.M., Kholkin, D.V., et al., A Multiagent Control System for Voltage and Reactive Power, Proc. 22nd Conf. “Relay Protection and Automation of Power Systems,” Moscow, 2014, pp. 243–252.Google Scholar
  21. 21.
    Belkacemi, R., Babalola, S., and Zarrabian, A., Experimental Implementation of Multi-Agent System Algorithm to Prevent Cascading Failure after N -1 -1 Contingency in Smart Grid Systems, IEEE Power & Energy Society General Meeting, Denver, 2015, pp. 1–5.Google Scholar
  22. 22.
    Panasetskii, D.A., Improving the Structure and Algorithms for Failure Protection Control of an Electric Power Station to Prevent Voltage Avalanche and Cascade Outing of Lines, Cand. Sci. Dissertation, Irkutsk: ISEM SB RAS, 2015.Google Scholar
  23. 23.
    Negenborn, R.R., De Schutter, B., and Hellendoorn, J., Multi-agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes, Eng. Appl. Artific. Intelligence, 2008, vol. 21, pp. 353–366.CrossRefGoogle Scholar
  24. 24.
    Zhukov, A.V. and Sidorov, D.N., A Modification of the Random Forest Algorithm for Classification of Nonstationary Streaming Data, Vest. YuUrGU, Mat. Modelir. Programmir., 2016, vol. 9, no. 4, pp. 86–95.Google Scholar
  25. 25.
    Zhukov, A.V., Sidorov, D.N., and Foley, A.M., Random Forest Based Approach for Concept Drift Handling, Commun. Comput. Inform. Sci., 2017, vol. 661, pp. 69–77.CrossRefGoogle Scholar
  26. 26.
    Voropai, N.I., Negnevitskii, M., Tomin, N.V., et al., An Intelligent System for Preventing Large Failures in Power Systems, Elektrichestvo, 2014, no. 8, pp. 1–7.Google Scholar
  27. 27.
    Geurts, P., Ernst, D., and Wehenkel, L., Extremely Randomized Trees, Machine Learning, 2006, vol. 63, no. 1, pp. 3–42.CrossRefzbMATHGoogle Scholar
  28. 28.
    Scornet, E., Random Forests and Kernel Methods, IEEE Trans. Inform. Theory, 2016, vol. 62, no. 3, pp. 1485–1500.MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Kundur, P., Power System Stability and Control, New York: McGraw Hill, 1994.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • N. I. Voropai
    • 1
    Email author
  • N. V. Tomin
    • 1
  • D. N. Sidorov
    • 1
  • V. G. Kurbatsky
    • 1
  • D. A. Panasetsky
    • 1
  • A. V. Zhukov
    • 1
  • D. N. Efimov
    • 1
  • A. B. Osak
    • 1
  1. 1.Melentiev Energy Systems Institute, Siberian BranchRussian Academy of SciencesIrkutskRussia

Personalised recommendations