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

 
PIIS000523100001845-3-1
DOI10.31857/S000523100001845-3
Publication type Article
Status Published
Authors
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Affiliation: Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences
Address: Russian Federation, Irkutsk
Journal nameAvtomatika i Telemekhanika
EditionIssue 10
Pages6-25
Abstract

   

Keywords
Received18.10.2018
Publication date25.10.2018
Number of characters585
Cite   Download pdf To download PDF you should sign in
Размещенный ниже текст является ознакомительной версией и может не соответствовать печатной

views: 1110

Readers community rating: votes 0

1. Voropaj N.I., Tomin N.V., Kurbatskij V.G. i dr. Kompleks intellektual'nykh sredstv dlya predotvrascheniya krupnykh avarij v ehlektroehnergeticheskikh sistemakh. Novosibirsk: Nauka, 2016.

2. Voropaj N.I., Saratova N.E. Analiz statistiki otkazov RZA na mikroprotsessornoj baze s tochki zreniya ikh ucheta pri modelirovanii kaskadnykh avarij // Problemy ehnergetiki. 2008. № 11/12 (1). C. 66–71.

3. Voropaj N.I. Snizhenie riskov kaskadnykh avarij v ehlektroehnergeticheskikh sistemakh. Novosibirsk: Izd-vo SO RAN, 2011.

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, PA USA, July, 2008.

5. Negnevitsky M. An Expert System Application for Clearing Overloads // Int. J. Power Energy Syst. 1995. V. 15. No. 1. P. 9–13.

6. Barkans E., Zhalostiba D. Zaschita ot razvalov i samovosstanovlenie ehnergosistem. Cheboksary: RITs «SRZAU», 2014.

7. Kessel P., Glavitsch H. Estimating the Voltage Stability of a Power System // IEEE Trans. Power Delivery. 1986. V. 1. No. 3. P. 346–354.

8. Vojtov O.N., Voropaj N.I., Gamm A.Z. i dr. Analiz neodnorodnostej ehlektroehnergeticheskikh sistem. Novosibirsk: Nauka, 1999.

9. Goh H. Comparative Study of Different Kalman Filter Implementations in Power System Stability // Amer. J. Appl. Sci. 2014. V. 11. No. 8. P. 1379–1390.

10. Karbalaei F., Soleymani H., Afsharnia S. A Comparison of Voltage Collapse Proximity Indicators // IPEC, 2010 Conf. Proc. 2010. P. 429–432.

11. Sayed Shah D.M. Voltage Stability in Electric Power System: A Practical Introduction. Berlin: Logos Verlag GmbH, 2015.

12. Kurbatsky V.G., Sidorov D.N., Spiryaev V.A., 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. P. 1–7.

13. Zhukov A., Tomin N., Sidorov D., Panasetsky D., Spirayev V. A Hybrid Artificial Neural Network for Voltage Security Evaluation in a Power System // Proc. 2015 Int. Youth Con. Energy (IYCE). Pisa, 2015. P. 1–8.

14. Kurbatsky V., Tomin N., Sidorov D., 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. P. 1–4.

15. Manov N.S., Khokhlov M.V., Chukreev Yu.Ya. i dr. Metody i modeli issledovaniya nadezhnosti ehlektroehnergeticheskikh sistem. Syktyvkar: Izd-vo Komi nauchnyj tsentr UrO RAN, 2010.

16. Kurbatskii V.G., Sidorov D.N., Spiryaev V.A., 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. V. 72. No. 7. P. 1405–1414.

17. Kalyani S., Shanti Swarup K. Design of Pattern Recognition System for Static Security Assessment and Classification // Patt. Anal. Appl. 2012. V. 15. P. 299– 311.

18. Jothinathan K., Ganapathy S. Transient Security Assessment in Power Systems Using Deep Neural Network // Int. J. Appl. Engin. Res. 2012. V. 10. No. 15. P. 787–790.

19. Diao R., Sun K., Vittal V., et al. Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements // IEEE Trans. Power Syst. 2009. V. 24. No. 2. P. 832–839.

20. Arkhipov I.L., Ivanov A.M., Kholkin D.V. i dr. Mul'tiagentnaya sistema upravleniya napryazheniem i reaktivnoj moschnost'yu // Tr. 22-j konf. «Relejnaya zaschita i avtomatika ehnergosistem». Moskva. 2014. S. 243–252.

21. Belkacemi R., Babalola S., Zarrabian A. Experimental Implementation of MultiAgent System Algorithm to Prevent Cascading Failure after N − 1 − 1 Contingency in Smart Grid Systems // IEEE Power & Energy Society General Meeting, Denver, CO. 2015. P. 1–5.

22. Panasetskij D.A. Sovershenstvovanie struktury i algoritmov protivoavarijnogo upravleniya EhEhS dlya predotvrascheniya laviny napryazheniya i kaskadnogo otklyucheniya linij // Dis. ... kand. tekhn. nauk: 05.14.02; Irkutsk: ISEhM SO RAN, 2015. 192 c.

23. Negenborn R.R., De Schutter B., Hellendoorn J. Multi-agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes // Engineer. Appl. Artific. Intelligence. 2008. V. 21. P. 353–366.

24. Zhukov A.V., Sidorov D.N. Modifikatsiya algoritma sluchajnogo lesa dlya klassifikatsii nestatsionarnykh potokovykh dannykh // Vestn. YuUrGU. Ser. Matem. modelirovanie i programmirovanie. 2016. T. 9. № 4. S. 86–95.

25. Zhukov A.V., Sidorov D.N., Foley A.M. Random Forest Based Approach for Concept Drift Handling // Commun. Comput. Inform. Sci. 2017. V. 661. P. 69–77.

26. Voropaj N.I., Negnevitskij M., Tomin N.V. i dr. Intellektual'naya sistema dlya predotvrascheniya krupnykh avarij v ehnergosistemakh // Ehlektrichestvo. 2014. № 8. S. 1–7.

27. Geurts P., Ernst D., Wehenkel L. Extremely Randomized Trees // Machine Learning. 2006. V. 63. No. 1. P. 3–42.

28. Scornet E. Random Forests and Kernel Methods // IEEE Trans. Inform. Theory. 2016. V. 62. No. 3. P. 1485–1500.

29. Kundur P. Power System Stability and Control. N.Y.: McGraw Hill, 1994.

Система Orphus

Loading...
Up