基于多任务学习的互联网电力系统异常检测

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xin Li, Zhaoyang Qu, Tong Yu, Ming Xie, Fu Yu, Wei Ding
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引用次数: 0

摘要

物联网(IoT)与电力系统的融合,即电力系统互联网(IoPS),极大地提高了能源分配和管理的效率和可靠性。然而,这种集成引入了复杂性和对可能破坏系统功能和安全性的异常的脆弱性。传统的异常检测方法虽然在一定程度上是有效的,但往往难以应对IoPS产生的数据的规模和多样性。基于此,本文提出了一种基于多任务学习(MTL)的异常检测框架。与单任务系统相比,MTL利用跨多个相关任务的共享表示,提高了检测性能和鲁棒性。我们提出了实现该框架的综合方法,包括模型体系结构、数据处理和评估度量。实验结果表明,我们的MTL方法在精度和效率上都明显优于传统方法。这项研究旨在提高IoPS安全性,同时为未来探索智能电网分析奠定基础。本文最后讨论了我们的研究结果对更具弹性的IoPS发展的影响,并提出了进一步研究的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Task Learning Empowered Anomaly Detection for Internet of Power Systems

The integration of the Internet of Things (IoT) with power systems, referred to as the Internet of Power Systems (IoPS), has significantly enhanced the efficiency and reliability of energy distribution and management. However, this integration introduces complexity and vulnerability to anomalies that can disrupt system functionality and security. Traditional anomaly detection methods, while effective to a degree, often struggle with the scale and diversity of data generated by IoPS. Motivated by this, we propose a novel anomaly detection framework based on multi-task learning (MTL) to address these challenges in this paper. MTL leverages shared representations across multiple related tasks, improving detection performance and robustness compared to single-task systems. We present a comprehensive methodology for implementing this framework, including model architecture, data handling, and evaluation metrics. Our experimental results demonstrate that our MTL approach significantly outperforms traditional methods in accuracy and efficiency. This research aims to advance IoPS security and, meanwhile, sets a foundational approach for future explorations into smart grid analytics. The paper concludes by discussing the implications of our findings for the development of more resilient IoPS and suggesting directions for further research.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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