Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid

Authors

  • Naiquan Xiao

DOI:

https://doi.org/10.62051/ijcsit.v3n1.10

Keywords:

Smart grid, Abnormal detection, Artificial intelligence

Abstract

The smart grid is an intelligent system of the power grid, which is a communication information support platform based on the coordinated development of the transmission network and various levels of power grids. It is a highly integrated system characterized by informationization, automation, and interactivity of various voltage levels, including transmission and transformation, distribution, and power dispatch. This article summarizes, analyzes, and summarizes the methods for detecting abnormal electricity consumption data in smart grids. It introduces the detection methods for abnormal electricity consumption data based on traditional technology and artificial intelligence technology, analyzes and elaborates on the basic principles and characteristics of each method, summarizes and looks forward to the challenges and future development trends faced by abnormal electricity consumption data detection in smart grids, and provides some reference for subsequent research.

References

Gopstein A, Nguyen C, O'Fallon C, et al. NIST framework and roadmap for smart grid interoperability standards, release 4.0[M]. Gaithersburg, MD, USA: Department of Commerce. National Institute of Standards and Technology, 2021.

Hashmi M, Hänninen S, Mäki K. Survey of smart grid concepts, architectures, and technological demonstrations worldwide[C]//2011 IEEE PES conference on innovative smart grid technologies Latin America (ISGT LA). IEEE, 2011: 1-7.

de Souza Savian F, Siluk J C M, Garlet T B, et al. Non-technical losses: A systematic contemporary article review[J]. Renewable and Sustainable Energy Reviews, 2021, 147: 111205.

Chi X, Yan C, Wang H, et al. Amplified locality-sensitive hashing-based recommender systems with privacy protection[J]. Concurrency and Computation: Practice and Experience, 2022, 34(14):

Zhong W, Yin X, Zhang X, et al. Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment[J]. Computer Communications, 2020, 157: 116-123.

Dileep G. A survey on smart grid technologies and applications[J]. Renewable Energy, 2020, 146: 2589-2625.

Liu Y, Guo W, Fan C I, et al. A practical privacy-preserving data aggregation (3PDA) scheme for smart grid[J]. IEEE Transactions on Industrial Informatics, 2018, 15(3): 1767-1774.

Hammerschmitt B K, da Rosa Abaide A, Lucchese F C, et al. Non-technical losses review and possible methodology solutions[C]. Proceedings of the 6th International Conference on Electric Power and Energy Conversion Systems (EPECS). IEEE, 2020: 64-68.

Gunduz M Z, Das R. Cyber-security on smart grid: Threats and potential solutions[J]. Computer Networks, 2020, 169: 107094.

Xu X, Mo R, Dai F, et al. Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud[J]. IEEE Transactions on Industrial Informatics, 2019, 16(9): 6172-6181.

Zhou C, Li A, Hou A, et al. Modeling methodology for early warning of chronic heart failure based on real medical big data[J]. Expert Systems with Applications, 2020, 151: 113361.

Li J, Cai T, Deng K, et al. Community-diversified influence maximization in social networks[J]. Information Systems, 2020, 92: 101522.

Lipčák P, Macak M, Rossi B. Big data platform for smart grids power consumption anomaly detection[C]. 2019 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2019: 771-780.

Cook A A, Mısırlı G, Fan Z. Anomaly detection for IoT time-series data: A survey[J]. IEEE Internet of Things Journal, 2019, 7(7): 6481-6494.

B. Rosner, “Percentage points for a generalized esd many-outlier procedure,” Technometrics, vol. 25, no. 2, pp. 165–172, 1983.

X. Li, C. P. Bowers, and T. Schnier, “Classification of energy consumption in buildings with outlier detection,” IEEE Transactions on Industrial Electronics, vol. 57, no. 11, pp. 3639–3644, 2009.

C. Fan, F. Xiao, and S. Wang, “Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques,” Applied Energy, vol. 127, pp. 1–10, 2014.

J.-S. Chou and A. S. Telaga, “Real-time detection of anomalous power consumption,” Renewable and Sustainable Energy Reviews, vol. 33, pp. 400–411, 2014.

M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting stealthy false data injection using machine learning in smart grid,” IEEE Systems Journal, vol. 11, no. 3, pp. 1644–1652, 2017.

Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks,” Cognitive Computation, vol. 12, no. 6, pp. 1381–1401, 2020.

S. Khalid, T. Khalil, and S. Nasreen, “A survey of feature selection and feature extraction techniques in machine learning,” in 2014 science and information conference. IEEE, 2014, pp. 372–378.

H. Karimipour, A. Dehghantanha, R. M. Parizi, K.-K. R. Choo, and H. Leung, “A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids,” IEEE Access, vol. 7, pp. 80 778–80 788, 2019.

A. A. Imayakumar, A. Dubey, and A. Bose, “Anomaly detection for primary distribution system measurements using principal component analysis,” in 2020 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2020, pp. 1–6.

M. Yue, “An integrated anomaly detection method for load forecasting data under cyberattacks,” in 2017 IEEE Power Energy Society General Meeting, 2017, pp. 1–5.

M. Yue, T. Hong, and J. Wang, “Descriptive analytics-based anomaly detection for cybersecure load forecasting,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 5964–5974, 2019.

A. A. Cook, G. Mısırlı, and Z. Fan, “Anomaly detection for iot timeseries data: A survey,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481–6494, 2020.

J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” Journal of the royal statistical society. series c(applied statistics), vol. 28, no. 1, pp. 100–108, 1979.

A. K. Jain, “Data clustering: 50 years beyond k-means,” Pattern recognition letters, vol. 31, no. 8, pp. 651–666, 2010.

G. Fenza, M. Gallo, and V. Loia, “Drift-aware methodology for anomaly detection in smart grid,” IEEE Access, vol. 7, pp. 9645–9657, 2019.

F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation forest,” in 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 413–422.

W. Mao, X. Cao, T. Yan, Y. Zhang et al., “Anomaly detection for power consumption data based on isolated forest,” in 2018 International Conference on Power System Technology (POWERCON). IEEE, 2018, pp. 4169–4174.

Z. Fengming, L. Shufang, G. Zhimin, W. Bo, T. Shiming, and P. Mingming, “Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network,” The Journal of China Universities of Posts and Telecommunications, vol. 24, no. 6, pp. 67–73, 2017.

J. Pereira and M. Silveira, “Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1275–1282.

V. Q. Nguyen, L. Van Ma, J.-y. Kim, K. Kim, and J. Kim, “Applications of anomaly detection using deep learning on time series data,” in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018, pp. 393–396.

D. Wu, B. Wang, D. Precup, and B. Boulet, “Multiple kernel learningbased transfer regression for electric load forecasting,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1183–1192, 2019.

Downloads

Published

15-06-2024

Issue

Section

Articles

How to Cite

Xiao, N. (2024). Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid. International Journal of Computer Science and Information Technology, 3(1), 63-72. https://doi.org/10.62051/ijcsit.v3n1.10

Similar Articles

1-10 of 61

You may also start an advanced similarity search for this article.