Abstract
Aiming at the problem of untimely fault detection caused by the large number and wide distribution of power grid equipment, this paper designs a cloud computing platform-based smart grid data anomaly detection method. This method uses cloud computing architecture to design a grid smart cloud platform, and realizes the aggregation and storage of large amounts of data in the cloud platform. After that, the STL decomposition method is used to decompose the electricity meter data on the cloud platform, and then the decomposed residual data is used for abnormal analysis to complete the detection of abnormal data in the smart grid. And the accuracy of the method is verified through experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, L., Shen, X., Zhang, F., Ren, M., Ge, B., Li, B.: Anomaly detection for power grid based on time series model. In: 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), New York, NY, USA, pp. 188–192 (2019)
Jian, Z., Guo, J., Xu, X., Wang, B., Yi, Y., Ye, H.: Risk analysis of wild fire on electric grid transmission lines considering cascading failure. In: 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, pp. 872–876 (2019)
Jianfeng, D., Jian, Q., Jing, W., Xuesong, W.: A vulnerability assessment method of cyber physical power system considering power-grid infrastructures failure. In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, pp. 1492–1496 (2019)
Qin, Y., Lou, Y.: Hydrological time series anomaly pattern detection based on isolation forest. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, pp. 1706–1710 (2019)
Chen, J., Chen, M., Wei, X., Chen, B.: Matrix differential decomposition-based anomaly detection and localization in NFV networks. IEEE Access 7, 29320–29331 (2019)
Radoglou-Grammatikis, P.I., Sarigiannidis, P.G.: An anomaly-based intrusion detection system for the smart grid based on CART decision tree. In: 2018 Global Information Infrastructure and Networking Symposium (GIIS), Thessaloniki, Greece, pp. 1–5 (2018)
Dinh, P.T., Lee, T., Canh, T.N., Dang, S.P., Chul Noh, S., Park, M.: Abnormal SDN switches detection based on chaotic analysis of network traffic. In: 2019 25th Asia-Pacific Conference on Communications (APCC), Ho Chi Minh City, Vietnam, pp. 250–255 (2019)
Andryukhin, E.V., Veligura, A.N.: Industrial network anomaly behavior detection via exponential smoothing model. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, pp. 1458–1462 (2018)
Wang, X., Zhao, T., Liu, H., He, R.: Power consumption predicting and anomaly detection based on long short-term memory neural network. In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, pp. 487–491 (2019)
Jia, R., Yang, Y., Grundy, J., Keung, J., Li, H.: A highly efficient data locality aware task scheduler for cloud-based systems. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), Milan, Italy, pp. 496–498 (2019)
De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: cloud, IoT, edge, and fog. IEEE Access 7, 150936–150948 (2019)
Fowley, F., Pahl, C., Jamshidi, P., Fang, D., Liu, X.: A classification and comparison framework for cloud service brokerage architectures. IEEE Trans. Cloud Comput. 6(2), 358–371 (2018)
Gill, S.S., Buyya, R.: Failure management for reliable cloud computing: a taxonomy, model, and future directions. Comput. Sci. Eng. 22(3), 52–63 (2020)
Acknowledgement
This work was supported by the I6000 migration to the cloud micro-application pilot construction project of the Information and Communication Branch of State Grid Anhui Electric Power Co., Ltd. Technical project (contract number: SGAHXT00XYXX2000121).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, J., Wu, S., Cao, W., Guo, Y., Gong, S. (2021). Smart Grid Data Anomaly Detection Method Based on Cloud Computing Platform. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-78609-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78608-3
Online ISBN: 978-3-030-78609-0
eBook Packages: Computer ScienceComputer Science (R0)