Real-time detection of electrical load anomalies through hyperdimensional computing

Wang, Xinlin; Flores, Robert; Brouwer, Jack; Papaefthymiou, Marios

doi:10.1016/j.energy.2022.125042

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…For power distribution systems, new AI techniques like hyperdimensional computing (HDC) are being used to find electrical load anomalies so that they do not have to rely on preprocessing and can focus on finding problems in real time [66]. Here, the experimental results showed better results than SVM, KNN, and LSTM, and the HDC approach works on raw data without preprocessing.…”

Section: Electrical Data Anomaliesmentioning

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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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: Electrical Data Anomaliesmentioning

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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AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response

Wang,

Bhandari

et al. 2023

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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This comprehensive review paper aims to provide an in-depth analysis of the most recent developments in the applications of artificial intelligence (AI) techniques, with an emphasis on their critical role in the demand side of power distribution systems. This paper offers a meticulous examination of various AI models and a pragmatic guide to aid in selecting the suitable techniques for three areas: load forecasting, anomaly detection, and demand response in real-world applications. In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, inclusive of reinforcement learning, in conjunction with the application of hybrid models and learning optimization strategies. This selection process is informed by the properties of load data and the specific scenarios that necessitate forecasting. Concerning anomaly detection, this paper provides an overview of the merits and limitations of disparate learning methods, fostering a discussion on the optimization strategies that can be harnessed to navigate the issue of imbalanced data, a prevalent concern in power system anomaly detection. As for demand response, we delve into the utilization of AI techniques, examining both incentive-based and price-based demand response schemes. We take into account various control targets, input sources, and applications that pertain to their use and effectiveness. In conclusion, this review paper is structured to offer useful insights into the selection and design of AI techniques focusing on the demand-side applications of future energy systems. It provides guidance and future directions for the development of sustainable energy systems, aiming to serve as a cornerstone for ongoing research within this swiftly evolving field.

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Dynamic XGBoost-based Quantile Predictor for Real-time Electricity Price Forecasting

Bao,

Wang,

Mahdavi

et al. 2023

2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)

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Real-time detection of electrical load anomalies through hyperdimensional computing

Cited by 6 publications

References 30 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

AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response

Dynamic XGBoost-based Quantile Predictor for Real-time Electricity Price Forecasting

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