Anomaly detection in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network

Ghanbari, Maryam; Kinsner, W.; Ferens, Ken

doi:10.1109/epec.2016.7771715

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Data integrity attacks [13,21,25,29,40,41,48,49,53,55,[59][60][61][62]70,75,76] Unusual consumption behaviors and measurements [6,24,27,32,34,35,38,46,52,67,68,[71][72][73] Network intrusions [16,18,19,56,63,69] Network infrastructure anomalies [14,15,17,20,22,33,39,47,58,64] Electrical data anomalies [7,23,26,36,…”

Section: Study Object Papermentioning

confidence: 99%

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A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

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The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

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Section: Study Object Papermentioning

confidence: 99%

“…Advanced metering infrastructure (AMI) is a key component because it is related to pricing, billing management, and consumption. It is targeted by cyber-attacks specializing in fraud and energy consumption patterns [24].…”

Section: Unusual Consumption Behaviors and Measurementsmentioning

confidence: 99%

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

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“…Some frequency domain-based methods have been proposed for anomaly detection in power load curves either using Fourier Transform [50] or Wavelet Transform [51]. However, to the best of our knowledge, no solution has been proposed using Hilbert-Huang Transform.…”

Section: Detecting Abnormal Energy Demand Behaviormentioning

confidence: 99%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

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“…noise generated during data transmission or from electrical appliances connected to the smart grid [79]. In [80], the identification of power consumption anomaly is handled by resorting to a multi-stage ANN-based solution. This latter incorporates a discrete wavelet transform to obtain the required features, a variance fractal dimension (VFD) operation applied on those features, an ANN scheme which exploits the VFD output to perform the training, and finally a threshold-based detection of the anomalous power consumption pattern.…”

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 in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network

Cited by 11 publications

References 6 publications

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

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

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