摘要翻译：
本文的主要主题是对网络异常的感知，这些异常包括从某些网络终端的无害阻抗变化到或多或少的明显电气故障，同时考虑到电缆随时间的退化。本文介绍了如何利用从几千赫到几兆赫的高频信号在配电网中获取这种异常的信息。考虑到宽带，我们依赖电力线调制解调器作为网络传感器。我们首先讨论了执行测量所需的前端架构，然后介绍了两种检测、分类和定位网络异常的算法。最后给出了仿真结果。它们验证了利用电力线调制解调器在智能电网中传感的概念，并显示了所提出算法的效率。
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英文标题：
《Smart Grid Monitoring Using Power Line Modems: Anomaly Detection and
  Localization》
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作者：
Federico Passerini and Andrea M. Tonello
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The main subject of this paper is the sensing of network anomalies that span from harmless impedance changes at some network termination to more or less pronounced electrical faults, considering also cable degradation over time. In this paper, we present how to harvest information about such anomalies in distribution grids using high frequency signals spanning from few kHz to several MHz. Given the wide bandwidth considered, we rely on power line modems as network sensors. We firstly discuss the front-end architectures needed to perform the measurement and then introduce two algorithms to detect, classify and locate the different kinds of network anomalies listed above. Simulation results are finally presented. They validate the concept of sensing in smart grids using power line modems and show the efficiency of the proposed algorithms.
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PDF链接：
https://arxiv.org/pdf/1807.05347