The usage of power system multi-model forecasting aided state estimation for cyber attack detection

THE PURPOSE. Smart electrical grids involve extensive use of information infrastructure. Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. State Estimation is used to identify the present power system operating state and eliminating metering errors and corrupted data. In particular, when a real measurement is replaced by a false one by a malefactor or a failure in the functioning of communication channels occurs, it is possible to detect false data and restore them. However, there is a class of cyberattacks, so-called False Data Injection Attack, aimed at distorting the results of the state estimation. The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.

METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. The multimodal state estimator consisted of three single state estimators, which produced single estimates using different forecasting models. In this paper only linear forecasting models were considered, such as autoregression model, vector autoregression model and Holt’s exponen tial smoothing. When we obtained the multi-model estimate as the weighted sum of the single-model estimates. Cyberattack detection was implemented through innovative and residual analysis. The analysis of the proposed algorithm performance was carried out by simulation modeling using the example of a IEEE 30-bus system in Matlab.

RESULTS. The paper describes an false data injection cyber attack and its specific impact on power system state estimation. A Multi - Model Forecasting-Aided State Estimation algorithm has been developed, which allows detecting cyber attacks and recovering corrupted data. Simulation of the algorithm has been carried out and its efficiency has been proved.

CONCLUSION. The results showed the cyber attack detection rate of 100%. The Multi-Model Forecasting-Aided State Estimation is an protective measure against the impact of cyber attacks on power system.

1. Kolosok IN, Gurina LА. Quality Assessment of SCADA and WAMS Data in the Case of Cyberattacks on Information and Communication Infrastructure of EPS . Information and mathematical technologies in science and management. 2020;1(17): 69-78.

2. Liu Y, Ning P and Reiter MK. False data injection attacks against state estimation in electric power grids. ACM Transactions on Information and System Security (TISSEC). 2011;14(1):13.

3. Bobba RB, Rogers K, Wang Q, et al. Detecting false data injection attacks on DC state estimation. Proceedings of First Workshop on Secure Control Systems (SCS 2010), Stockholm, Sweden (April 2010).

4. Karimipour H and Dinavahi V. On false data injection attack against dynamic state estimation on smart power grids. 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE). 2017. pp. 388-393.

5. Moslemi R., Mesbahi A., and Velni J. M. A fast, decentralized covariance selectionbased approach to detect cyber attacks in smart grids. IEEE Transactions on Smart Grid, 2017.

6. Li S, lmaz YY and Wang XD. Quickest detection of false data injection attack in widearea smart grids. IEEE Trans. Smart Grid. 2015;6(6):2725-2735.

7. Esmalifalak M, Shi G, Han Z, et al. Bad data injection attack and defense in electricity market using game theory study. IEEE Trans. Smart Grid. 2013;4(1):160-169.

8. Kulikov АL, Sharafeev Т.R, Osokin V.U. Methods of Detecting Cyber Attacks and Analysis of Scenarios of Cyber Attacks on the Power System. Bulletin NGIEI. 2017(10 (77)).

9. Lukicheva IА, Kulikov АL. Povyshenie tochnosti otsenki sostoyaniya elektricheskoi seti v usloviyakh kiberatak s ispol'zovaniem mediannoi fil'tratsii. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta, 2019;23:2 (145).

10. Gamm AZ. Statistical methods for the power systems state estimation. M.: Nauka, 1976, 220 p.

11. Zhao A. Comer-Exposito, Netto M, et al. Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work. IEEE Transactions on Power Systems. 2019;34(4):3188–3198. doi: 10.1109/TPWRS.2019.2894769.

12. Do Coutto Filho, Milton Brown, and Julio Cesar Stacchini de Souza. Forecastingaided state estimation-Part I: Panorama. IEEE Transactions on Power Systems 24.4 (2009):1667-1677.

13. Hirman S. YAD. Radioelektronnye sistemy: Osnovy postroeniya i teoriya. Spravochnik. Electronic systems: Basics of construction and theory. Izd. 2-e pererab. i dop. M.: Radiotekhnika, 2007. 512 p.

14. Da Silva A.L, Do Coutto Filho M, Cantera J. An efficient dynamic state estimation algorithm including bad data processing // IEEE transactions on Power Systems. 1987;2(4):1050–1058.

15. Pel Y, Biswas S, Fussell DS, et al. An elementary introduction to Kalman filtering. Communications of the ACM 62.11. 2019. pp. 122–133.

16. Geetha SJ, Chakrabarti S, Rajawat K, Terzija V. An asynchronous decentralized forecasting-aided state estimator for power systems. IEEE Transactions on Power Systems. 2019 Jan 31;34(4):3059-68.

17. Liao Yizheng, Yang Weng, Guangyi Liu, Ram Rajagopal. Urban mv and lv distribution grid topology esti-mation via group lasso. IEEE Transactions on Power Systems 34.1. 2018. pp. 12–27.

18. Administration BP. (2012) Wind generation total load in the bpabalancing authority. [Online]. Available at http://transmission.bpa.gov/business/operations/wind

19. Meliopoulos A, Madani V, Novosel D, et al. Synchrophasor measurement accuracy char-acterization. North American Synchro Phas-or Initiative Performance &Standards Task Team (Consortium for Electric Reliability Technology-Solutions). 2007. V. 10.