Detection of Abnormal Load Consumption in the Power Grid Using Clustering and Statistical Analysis

Cuper, Matúš; Lóderer, Marek; Rozinajová, Viera

doi:10.1007/978-3-030-33607-3_50

Cited by 1 publication

(2 citation statements)

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“…M. Cuper et al reviewed existing work on anomaly detection schemes and introduced systems and monitoring models. A new framework was proposed in the study aimed at accurately and timely detecting power consumption anomalies using sensor processing, smart meter readings, machine learning, and blockchain [9]. M. Li et al narrowed down potential abnormal customers in large power grid datasets by clustering discrete time series, and calculated user anomaly scores through statistical methods to accurately identify users with abnormal load consumption [10].…”

Section: Related Workmentioning

confidence: 99%

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Zhang,

Zhu

2024

IEEE Access

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With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved K-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

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Section: Related Workmentioning

confidence: 99%

“…u is the main eigenvectors, corresponding to a maximum eigenvalue of 1 σ . The noise ( ) d t is used in the original data of the field road to generate confused protective gear, and the noise processing process is represented as follows (9).…”

Section: ( )mentioning

confidence: 99%

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Zhang,

Zhu

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Detection of Abnormal Load Consumption in the Power Grid Using Clustering and Statistical Analysis

Cited by 1 publication

References 12 publications

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

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