Мережевий підхід при дослідженні каскадних ефектів критичних інфраструктур
DOI:
https://doi.org/10.35681/1560-9189.2024.26.2.316908Ключові слова:
критична інфраструктура, каскадний збій, теорія графів, мережа Баєса, мережа Петрі, ланцюг Маркова, онтологіяАнотація
Каскадний збій у роботі критичної інфраструктури призводить до негативних наслідків, тому важливо вчасно виявити та провести превентивні дії для зменшення наслідків каскаду. У статті представлено аналіз можливостей мережевого підходу при побудові та дослідженні моделі каскаду на основі теорії графів. За допомогою метрик якості графів, можливо визначити центральність та важливість вузлів моделі, розрахувати імовірності переходів між вузлами, настання критичних подій, дослідити різні сценарії розвитку. Графічна модель енергомережі, являє взаємопов’язану мережу вузлів і кінцевих обмежень (лінія електропередачи, трансформаторна підстанція, імпеданс та інші). В статті розглянуто застосування різних мережевих підходів — мережі Баєса, Петрі, Маркова та наведено результати порівняльного аналізу їх можливостей при виникненні каскаду. Це дозволяє більш досконало адаптувати мережеві методи до конкретних потреб моделювання та сформувати вимоги до відповідних програмних засобів.
Посилання
Zakon Ukrayiny "Pro krytychnu infrastrukturu" # 1882-IX vid 16.11.2021. URL:https://zakon.rada.gov.ua/laws/show/1882-20#Text
Rinaldi S.M., Peerenboom J.P., Kelly T.K. (2001). Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies. IEEE Control Systems Magazine, 21 (6), 11–25. DOI:10.1109/37.969131
Kadri F., Birregah B., Chatelet E. (2014). The Impact of Natural Disasters on Critical Infrastructures: A Domino Effect-based Study. Journal of Homeland Security and Emergency Management, De Gruyter, 11(2), pp. 217-241. DOI:10.1515/jhsem-2012-0077
Darbra R.M., Palacios A., Casal J. (2010). Domino effect in chemical accidents: main features and accident sequences. Journal of Hazardous Materials, 83(1–3), pp. 565–573. doi: 10.1016/j.jhazmat.2010.07.061
Chen C., Reniers G., Khakzad N. (2020). A thorough classification and discussion of approaches for modeling and managing domino effects in the process industries. Safety Science, 125, pp. 1-23. doi: 10.1016/j.ssci.2020.104618
Zakon Ukrayiny "Pro obyekty pidvyshchenoyi nebezpeky" # 2245-III vid 18.01.2001. URL:https://zakon.rada.gov.ua/laws/show/2245-14#Text
Kinney R., Crucitti P., Albert R., Latora V. (2005). Modeling cascading failures in the North American power grid. The European Physical Journal B-Condensed Matter and Complex Systems, 46(1), 101-107. DOI: 10.1140/epjb/e2005-00237-9
Koc Y., Verma T., Araujo N.A.M., Warnier M. (2013). MATCASC: A tool to analyse cascading line outages in power grids. 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES), pp. 143–148. doi: 10.1109/IWIES.2013.6698576
Magdi S. Mahmoud, Yuanqing Xia, (2019). Cascading failures in power grids. IEEE Potentials, 28(5), pp. 24-30. doi: 10.1109/MPOT.2009.933498
Xie B., Tian X., Kong L., Chen W. The Vulnerability of the Power Grid Structure: A System Analysis Based on Complex Network Theory. Sensors. 2021; 21(21):7097. doi: 10.3390/s21217097
Panteli M., Mancarella P., Trakas D.N., Kyriakides E., Hatziargyriou N.D. (2017). Metrics and Quantification of Operational and Infrastructure Resilience. Power Systems. IEEE Transactions on Power Systems, 32(6), pp. 4732-4742. doi: 10.1109/TPWRS.2017.2664141
Nan C., Sansavini G., Kroger W., Heinimann H.R. A Quantitative Method for Assessing the Resilience of Infrastructure Systems. Proceedings of the Probabilistic Safety Assessment and Management Conference, PSAM12, 2014.
Poulin C., Kane M.B. Infrastructure resilience curves: Performance measures and summary metrics. Reliability Engineering & System Safety, Volume 216, December 2021, 107926. doi: 10.1016/j.ress.2021.107926
The total amount of damage caused to Ukraine’s infrastructure due to the war has increased to almost $138 billion. 24 January 2023. URL: https://kse.ua/about-the-school/news/the-total-amount-of-damage-caused-to-ukraine-s-infrastructure-due-to-the-war-has-increased-to-almost-138-billion/
Hern´andez J.M., Mieghem P.V. (2011). Classification of graph metrics. URL: https://www.nas.ewi.tudelft.nl/people/Piet/papers/TUDreport20111111_MetricList.pdf
Oehlers M., Fabian B. Graph Metrics for Network Robustness—A Survey. Mathematics 2021, 9(8), 895; doi: 10.3390/math9080895
Di Nardo A., Giudicianni C., Greco R., Herrera M., Santonastaso G.F. Applications of Graph Spectral Techniques to Water Distribution Network Management. Water. 2018; 10(1):45. doi: 10.3390/w10010045
Puuska S., Kansanen K., Rummukainen L., Vankka J. (2015). Modelling and real-time analysis of critical infrastructure using discrete event systems on graphs. 2015 IEEE International Symposium on Technologies for Homeland Security (HST). DOI:10.1109/THS.2015.7225330
Stergiopoulos G., Kotzanikolaou P., Theocharidouc M., Gritzalis D. (2015). Risk mitigation strategies for critical infrastructuresbased on graph centrality analysis. International Journal of Critical Infrastructure Protection, 10 (September 2015), pp. 34–44. DOI:10.1016/j.ijcip.2015.05.003
Guze S. Graph Theory Approach to the Vulnerability of Transportation Networks. Algorithms. 2019; 12(12):270. doi: 10.3390/a12120270
Zelin Wan, Yash Mahajan, Beom Woo Kang, Terrence J. Moore, Jin-Hee Cho. A Survey on Centrality Metrics and Their Implications in Network Resilience. doi: 10.1109/ACCESS.2021.3094196
Cuadra L., Salcedo-Sanz S., Del Ser J., Jimenez-Fernandez S., Geem Z.W. A Critical Review of Robustness in Power Grids Using Complex Networks Concepts. Energies. 2015; 8(9):9211-9265. doi: 10.3390/en8099211
Vanis M., Lokaj Z., Srotyr M. A Novel Algorithm for Merging Bayesian Networks. Symmetry. 2023; 15(7):1461. doi: 10.3390/sym15071461
Daly R., Qiang S. & Aitken S. (2011). Learning Bayesian Networks: Approaches and Issues., Knowledge Engineering Review, vol. 26, no. 2, pp. 99-127. doi: 10.1017/S0269888910000251
Srinivas S. (1993). A Generalization of the Noisy-Or Model. Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 208-215. doi: 10.1016/B978-1-4832-1451-1.50030-5
Kitson N.K., Constantinou A.C., Zhigao G., Liu Y., Chobtham K. (2023). A survey of Bayesian Network structure learning. Artificial Intelligence Review 56, 8721-8814. DOI: 10.1007/s10462-022-10351-w
Khakzad N. A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks. Modelling. 2021; 2(2):240-258. doi: 10.3390/modelling2020013
Cerotti D., Codetta-Raiteri D., Dondossola G., Egidi L., Franceschinis G., Portinale L., Terruggia R. Evidence-Based Analysis of Cyber Attacks to Security Monitored Distributed Energy Resources. Applied Sciences. 2020; 10(14):4725. doi: 10.3390/app10144725
Wright M., Chizari H., Viana T. A Systematic Review of Smart City Infrastructure Threat Modelling Methodologies: A Bayesian Focused Review. Sustainability. 2022; 14(16):10368. doi: 10.3390/su141610368
Eldosouky A., Saad W., Mandayam N. (2021). Resilient Critical Infrastructure: Bayesian Network Analysis and Contract-Based Optimization. Reliability Engineering & System Safety Volume 205, January 2021, 107243. doi: 10.1016/j.ress.2020.107243
He X. (2013). A comprehensive survey of petri net modeling in software engineering. International Journal of Software Engineering and Knowledge Engineering, 23(5), 589-625. DOI: 10.1142/S021819401340010X
Murata T. (1989). Petri nets: properties, analysis and applications. Proceedings of the IEEE, 77 (4), 541–580, DOI: 10.1109/5.24143
Ge M., Rossi B., Chren S., Blanco J.M. Petri Nets for Smart Grids: The Story So Far. SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, pp. 661 – 670. doi: 10.1145/3605098.3635989
Kamil M.Z., Taleb-Berrouane M., Khan F., Ahmed S. (2019). Dynamic domino effect risk assessment using Petri-nets. Process Safety and Environmental Protection Volume 124, April 2019, pp. 308-316. doi: 10.1016/j.psep.2019.02.019
Rui Li, Bertr Decocq, Anne Barros, Yiping Fang, Zhiguo Zeng. Petri Net-Based Model for 5G and Beyond Networks Resilience Evaluation. 2022 25th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Mar 2022, Paris, France. pp.131-135, 10.1109/ICIN53892.2022.9758134
Lu W., Besanger Y., Zamai E., Radu D. (2006). Blackouts: Description, analysis and classification. Proceedings of the 6th WSEAS International Conference on Power Systems, Lisbon, Portugal, pp. 429-434, 2006, September.
Chao Zhai. A Robust Optimization Approach for Terminating the Cascading Failure of Power Systems. doi: 10.48550/arXiv.1907.13452
Nakarmi U., Rahnamay-Naeini M. (2020). A Markov Chain Approach for Cascade Size Analysis in Power Grids based on Community Structures in Interaction Graphs. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 1–6. DOI:10.1109/PMAPS47429.2020.9183579
Farid Kadri, Eric Chatelet. Domino Effect Analysis and Assessment of Industrial Sites: A Review of Methodologies and Software Tools. International Journal of Computers and Distributed Systems, 2013, 02 (III), pp.1-10. hal-01026495f
Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng Chau. Evaluating Graph Vulnerability and Robustness using TIGER. doi: 10.48550/arXiv.2006.05648
Naeem Md Sami, Mia Naeini. Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review. Electric Power Systems Research, Volume 232, July 2024, 110415. doi: 10.1016/j.epsr.2024.110415
Chadaga S., Wu X., Modiano E. (2023). Power Failure Cascade Prediction using Graph Neural Networks. 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, United Kingdom, 2023, pp. 1-7. doi: 10.1109/SmartGridComm57358.2023.10333943
Varbella A., Gjorgiev B., Sansavini G. (2023). Geometric deep learning for online prediction of cascading failures in power grids. Reliability Engineering & System Safety Volume 237, September 2023, 109341. doi: 10.1016/j.ress.2023.109341
More than 700 critical infrastructure facilities damaged in Ukraine since the beginning of full-scale russian invasion: Yevhenii Yenin. Ministry of Internal Affairs of Ukraine, posted 28 December 2022 11:33. URL: https://www.kmu.gov.ua/en/news/v-ukraini-z-pochatku-povnomasshtabnoho-vtorhnennia-rf-urazheno-ponad-700-obiektiv-krytychnoi-infrastruktury-ievhenii-ienin
Koval O.V. Metody ta zasoby komp'yuternoho modelyuvannya stsenariyiv analitychnoyi diyalnosti: dys. … d-ra tekhn. nauk: 01.05.02. Kyiv: Instytut problem modelyuvannya v enerhetytsi im. H.YE. Pukhova NAN Ukrayiny, 2021. 440 p.
Koval O., Dodonov O.H., Senchenko V.R., Putyatin V.H., Boychenko A.V. Metodolohichni ta tekhnolohichni aspekty komp'yuternoho modelyuvannya stsenariyiv pryynyattya rishen?. Matematychni mashyny i systemy. Kyyiv, IPMMS NAN Ukrayiny, 2023. № 3. P. 65–88. ISSN 1028-9763. doi: 10.34121/1028-9763-2023-3-65-88
Koval O.V., Dodonov O.G., Senchenko V.R., Boychenko A.V. Modelyuvannya stsenariyiv analitychnoyi diyal?nosti na osnovi notatsiyi BPMN OWL. Reyestratsiya, zberezhennya i obrobka danykh. 2020. № 1(22). pp. 31–48. doi: 10.35681/1560-9189.2020.1.1.207782
Thomas R. Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition, Knowledge Acquisition, 5(2):199-220, 1993. URL: https://tomgruber.org/writing/ontolingua-kaj-1993.pdf
Natalya F. Noy, Deborah L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology. 2001. URL: https://protege.stanford.edu/publications/ontology_development/ontology101.pdf
Asuncion Gomez Perez, V. Richard Benjamins. Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5) Stockholm, Sweden, August 2, 1999.
Fernandez M., Gomez-Perez A., Juristo N. METHONTOLOGY: From Ontological Art Towards Ontological Engineering. Spring Symposium on Ontological Engineering (AAAI): Technical Report SS-97-06, 1997.
