Anomaly Detection method of Smart Meters data based on GMM-LDA clustering feature Learning and PSO Support Vector Machine

Zhang, Leping; Xiao, Yong; Li, Shuangquan; Zhu, Chunhui

doi:10.1109/ispec48194.2019.8974989

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…Anomalies were calculated based on an anomaly consumption score. Zhang et al [46] utilised Gaussian Mixture Model (GMM) and Linear Discriminant Analysis (LDA) for detecting abnormal power consumption. The evaluation of the proposed GMM-LDA approach showed higher accuracy than SVM-based approaches.…”

Section: ) Unsupervised Techniquesmentioning

confidence: 99%

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

et al. 2023

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The smart grid integrates Information and Communication Technologies (ICT) into the traditional power grid to manage the generation, distribution, and consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in the grid. Identifying anomalous behaviours helps to discover unusual user power consumption, faulty infrastructure, power outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on smart meter data has shown remarkable results in anomaly detection. However, traditional ML-based anomaly detection requires smart meters to share local data with a central server, which raises concerns regarding data security and user privacy. Server-based model training faces additional challenges, such as the requirement of centralised computing power, reliable network communication, large bandwidth capacity, and latency issues, all of which affect the real-time anomaly detection performance. Motivated by these concerns, we propose a Federated Learning (FL)-based smart grid anomaly detection scheme where ML models are trained locally in smart meters without sharing data with a central server, thus ensuring user privacy. In the proposed approach, a global model is downloaded from the server to smart meters for ondevice training. After local training, local model parameters are sent to the server to improve the global model. We secure the model parameter updates from adversaries using the SSL/TLS protocol. Using standard datasets, we investigate the anomaly detection performance of federated learning and observe that FL models achieve anomaly detection performance comparable to centralised ML models while ensuring user privacy. Further, our study shows that the proposed FL-based models perform efficiently in terms of memory, CPU usage, bandwidth and power consumption at edge devices and are suitable for implementation in resourceconstrained environments, such as smart meters, for anomaly detection.

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Section: ) Unsupervised Techniquesmentioning

confidence: 99%

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

et al. 2023

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“…Trevizan et.al [40] proposed anomaly detection using Reed-Xaoli (RX) algorithm and a Chi-squared test for the detection FDI attacks in power system measurements.Deep learning techniques like deep auto-encoders were implemented in the distributed grid network [41] to address PMU data manipulation attacks. In [42] the unsupervised learning model was proposed based on GMM-LDA clustering for feature learning and PSO-SVM on smart meter data from AMI to trace abnormal power consumption. The integrated learning model performed better than the supervised SVM learning.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

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“…In the same direction, in [108], a genetic SVM model is proposed to detect abnormal consumption data and suspicious customers, in which a genetic algorithm is combined with SVM. While in [109], Zhang et al fuse SVM and particle swarm optimization for detecting abnormal power consumption in advanced metering infrastructures. On the other side, in [110], a decision tree based solution is introduced to learn energy consumption anomalies triggered by fraud energy usage.…”

Section: Supervised Detection (S)mentioning

confidence: 99%

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

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Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

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Anomaly Detection method of Smart Meters data based on GMM-LDA clustering feature Learning and PSO Support Vector Machine

Cited by 19 publications

References 14 publications

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

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