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Dynamic Security Assessment for Power System Under Cyber-Attack

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Abstract

In recent years, power systems have become more dependent on new technological advancement in the communication network to send and receive data and commands through the wide area power network. This dependence has created a new threat to the network, known as a cyber-attack. Such attacks could lead to blackouts and the consequences on the security of the power system would be severe. This study presents a new approach, which aims at assessing the dynamic behavior of the test system’s model under a cyber-attack contingency. The methodology used in this study was based on the scenarios of cyber-attacks on the protection relay devices to generate the database for dynamic security assessment. Then, a data mining framework was used for the database preparation and classification via feature selection algorithm and decision tree classifier. The results of the modified IEEE 30-bus test system model in this study showed a high accuracy of 99.537%, and a short time frame that makes this application suitable for real-time application to protect the power network from an insecure state and ensure that the power system remains reliable.

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References

  1. Glenn C, Sterbentz D, Wright A (2016) Cyber threat and vulnerability analysis of the us electric sector. Idaho National Lab (INL), Idaho Falls

    Book  Google Scholar 

  2. Zetter K (2016) Inside the cunning, unprecedented hack of Ukraine’s power grid, Wired. Available at: https://www.wired.com/2016/03/inside-cunning-unprecedented-hack-ukraines-power-grid/

  3. Gorman S (2009) Electricity grid in US penetrated by spies, Wall Str J 8:A1

    Google Scholar 

  4. Cleveland FM (2008) Cyber security issues for advanced metering infrastructure (AMI). In: 2008 IEEE power and energy society general meeting conversion and delivery of electrical energy in the 21st century, 15 p

  5. Rahman A, Ali M (2018) Analysis and evaluation of wireless networks by implementation of test security keys. In: International conference for emerging technologies in computing. Springer, Cham, pp 107–126

  6. Pan S, Morris T, Adhikari U (2015) Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data. IEEE Trans Ind Inf 11:650–662

    Article  Google Scholar 

  7. Wang JW, Rong LL (2009) Cascade-based attack vulnerability on the US power grid. Saf Sci 47:1332–1336

    Article  Google Scholar 

  8. Sridhar S, Hahn A, Govindarasu M (2012) Cyber-physical system security for the electric power grid. Proc IEEE 100:210–224

    Article  Google Scholar 

  9. Wang J, Hui LC, Yiu S, Wang EK, Fang J (2017) A survey on cyber attacks against nonlinear state estimation in power systems of ubiquitous cities. Pervasive Mob Comput 39:52–64

    Article  Google Scholar 

  10. Liu Y, Ning P, Reiter MK (2011) False data injection attacks against state estimation inelectric power grids. ACM Trans Inf Syst Secur (TISSEC) 14(1):13

    Article  Google Scholar 

  11. Hink RCB, Beaver JM, Buckner MA, Morris T, Adhikari U, Pan S (2014) Machine learning for power system disturbance and cyber-attack discrimination. In: 2014 7th international symposium on resilient control systems (ISRCS), pp 1–8

  12. Pan S, Morris T, Adhikari U (2015) Developing a hybrid intrusion detection system using data mining for power systems. IEEE Trans Smart Grid 6:3104–3113

    Article  Google Scholar 

  13. Morison K, Wang L, Kundur P (2004) Power system security assessment. IEEE Power Energ Mag 2:30–39

    Article  Google Scholar 

  14. Xu L, Jiang C, Wang J, Yuan J, Ren Y (2014) Information security in big data: privacy and data mining. Access IEEE 2:1149–1176

    Article  Google Scholar 

  15. Al-Masri AN, Ab Kadir MZA, Hizam H, Mariun N (2013) A novel implementation for generator rotor angle stability prediction using an adaptive artificial neural network application for dynamic security assessment. IEEE Trans Power Syst 28:2516–2525

    Article  Google Scholar 

  16. Kucuktezcan CF, Genc VI (2012) A new dynamic security enhancement method via genetic algorithms integrated with neural network based tools. Electr Power Syst Res 83:1–8

    Article  Google Scholar 

  17. Swarup KS (2008) Artificial neural network using pattern recognition for security assessment and analysis. Neurocomputing 71:983–998

    Article  Google Scholar 

  18. Luo F, Dong Z, Chen G, Xu Y, Meng K, Chen Y et al (2015) Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment. IEEE Trans Ind Inf 11:416–426

    Article  Google Scholar 

  19. Alvarez JMG, Mercado PE (2007) A new approach for power system online DSA using distributed processing and fuzzy logic. Electr Power Syst Res 77:106–118

    Article  Google Scholar 

  20. Sun K, Likhate S, Vittal V, Kolluri VS, Mandal S (2007) An online dynamic security assessment scheme using phasor measurements and decision trees. Power Syst IEEE Trans 22:1935–1943

    Article  Google Scholar 

  21. He M, Zhang J, Vittal V (2012) A data mining framework for online dynamic security assessment: decision trees, boosting, and complexity analysis. In: Innovative smart grid technologies (ISGT), 2012 IEEE PES, pp 1–8

  22. He M, Vittal V, Zhang J (2013) Online dynamic security assessment with missing PMU measurements: a data mining approach. IEEE Trans Power Syst 28:1969–1977

    Article  Google Scholar 

  23. He M, Zhang J, Vittal V (2013) Robust online dynamic security assessment using adaptive ensemble decision-tree learning. IEEE Trans Power Syst 28:4089–4098

    Article  Google Scholar 

  24. Liu C, Sun K, Rather ZH, Chen Z, Bak CL, Thogersen P et al (2014) A systematic approach for dynamic security assessment and the corresponding preventive control scheme based on decision trees. IEEE Trans Power Syst 29:717–730

    Article  Google Scholar 

  25. Xu Y, Dong ZY, Zhao JH, Zhang P, Wong KP (2012) A reliable intelligent system for real-time dynamic security assessment of power systems. IEEE Trans Power Syst 27:1253–1263

    Article  Google Scholar 

  26. Al-Gubri QA, Ariff MMAM, Saeh IS (2016) Performance analysis of machine learning algorithms for power system dynamic security assessment. In: 4th IET clean energy and technology conference (CEAT 2016), ‏2016, pp 37–42

  27. Kamwa I, Grondin R, Loud L (2001) Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques. IEEE Trans Power Syst 16:526–536

    Article  Google Scholar 

  28. Kamwa I, Samantaray SR, Joos G (2009) Development of rule-based classifiers for rapid stability assessment of wide-area post-disturbance records. IEEE Trans Power Syst 24:258–270

    Article  Google Scholar 

  29. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington, MA, USA

    MATH  Google Scholar 

  30. Vaiman M, Bell K, Chen Y, Chowdhury B, Dobson I, Hines P et al (2012) Risk assessment of cascading outages: methodologies and challenges. IEEE Trans Power Syst 27:631

    Article  Google Scholar 

  31. Konstantinou C, Maniatakos M (2015) Impact of firmware modification attacks on power systems field devices. IEEE Int Conf Smart Grid Commun (SmartGridComm) 2015:283–288

    Article  Google Scholar 

  32. Romeis C, Fuchs J, Jaeger J, Krebs R (2011) Innovative strategies for protection security assessment. IEEE Power Energy Soc Gen Meet 2011:1–6

    Google Scholar 

  33. Romeis C, Jaeger J (2013) Dynamic protection security assessment, a technique for blackout prevention. In: PowerTech (POWERTECH), 2013 IEEE grenoble, pp 1–6

  34. Kannan SS, Ramaraj N (2010) A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowl Based Syst 23:580–585

    Article  Google Scholar 

  35. Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: ICML, pp 856–863

  36. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224

    MathSciNet  MATH  Google Scholar 

  37. Kaladhar D, Chandana B, Kumar PB (2011) Predicting cancer survivability using classification algorithms. LMT 34:96–106

    Google Scholar 

  38. Doetsch P, Buck C, Golik P, Hoppe N, Kramp M, Laudenberg J et al (2009) Logistic model trees with AUC split criterion for the KDD cup 2009 small challenge. In: KDD Cup, pp 77–88

  39. Gupta D, Malviya A, Singh S (2012) Performance analysis of classification tree learning algorithms. Int J Comput Appl 55:39–44

    Google Scholar 

  40. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59:161–205

    Article  MATH  Google Scholar 

  41. Demetriou P, Asprou M, Quiros-Tortos J, Kyriakides E (2017) Dynamic IEEE test systems for transient analysis. IEEE Syst J 11:2108–2117

    Article  Google Scholar 

  42. Zuo C, Wang B, Zhang M, Khanwala MA, Dang S (2015) Power flow analysis using PowerWorld: a comprehensive testing report. Int Conf Fluid Power Mechatron (FPM) 2015:997–1002

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for the award that enabled this research to be conducted under the Grant U424, U455 and to the Ministry of Communications (Iraq) for the technical support.

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Authors and Affiliations

  1. Department of Electrical Power Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, 86400, Parit Raja, Malaysia

    Qusay A. Al-Gburi & Mohd Aifaa Mohd Ariff

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  1. Qusay A. Al-Gburi
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  2. Mohd Aifaa Mohd Ariff
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Correspondence to Qusay A. Al-Gburi.

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Al-Gburi, Q.A., Mohd Ariff, M.A. Dynamic Security Assessment for Power System Under Cyber-Attack. J. Electr. Eng. Technol. 14, 549–559 (2019). https://doi.org/10.1007/s42835-019-00084-2

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  • DOI: https://doi.org/10.1007/s42835-019-00084-2

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