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arXiv 2306.02473cs.CRcs.LG

智能电网系统中的异常检测技术:综述

Anomaly Detection Techniques in Smart Grid Systems: A Review

Tennessee Tech University(田纳西理工大学)

Shampa Banik, Sohag Kumar Saha, Trapa Banik, S M Mostaq Hossain

AI总结:

本文综述智能电网异常检测的最新进展,从多方面分类现有研究、分析挑战与不足,并指出未来研究方向。

摘要:

智能电网数据可在众多领域进行异常检测评估,包括网络安全、故障检测、窃电等。这些奇怪的异常行为可能由多种原因引起,包括消费者特殊的用电模式、电网基础设施故障、停电、外部网络攻击或能源欺诈。近年来,智能电网异常检测引起了研究人员的广泛关注,并被广泛应用于多个高影响力领域。智能电网中最重要的挑战之一是针对多种形式的异常行为实施高效的异常检测。本文对智能电网背景下异常检测的最新研究进展进行了范围性综述。为深入理解和审视迄今为止的研究挑战,我们从多个方面对研究进行了分类。最后,在分析了所综述论文中的空白之后,简要给出了智能电网系统异常检测未来研究的方向。

英文摘要:

Smart grid data can be evaluated for anomaly detection in numerous fields, including cyber-security, fault detection, electricity theft, etc. The strange anomalous behaviors may have been caused by various reasons, including peculiar consumption patterns of the consumers, malfunctioning grid infrastructures, outages, external cyber-attacks, or energy fraud. Recently, anomaly detection of the smart grid has attracted a large amount of interest from researchers, and it is widely applied in a number of high-impact fields. One of the most significant challenges within the smart grid is the implementation of efficient anomaly detection for multiple forms of aberrant behaviors. In this paper, we provide a scoping review of research from the recent advancements in anomaly detection in the context of smart grids. We categorize our study from numerous aspects for deep understanding and inspection of the research challenges so far. Finally, after analyzing the gap in the reviewed paper, the direction for future research on anomaly detection in smart-grid systems has been provided briefly.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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