用于智能电网监测的电力线调制解调器电缆诊断
Cable Diagnostics with Power Line Modems for Smart Grid Monitoring
摘要：
电缆状况的远程监控是下一代智能电网的基本特征，其特征在于能够持续地监视和控制电网基础设施。在本文中，我们提出了一种利用电力线调制解调器（PLM）作为传感器来监测电缆健康状况的技术。我们设想，所有或大部分这些PLM已经被部署用于数据通信目的，并集中在智能电网中的配电网或邻域网络。对于这样的设置，我们提出了一个基于机器学习（ML）的自动电缆诊断框架，通过不断监测电缆状态来识别，评估和定位可能的降级。作为我们技术的一部分，我们还在PLM中合成了最先进的反射测量方法，以提取有益的特征，从而有效地实现我们提出的ML解决方案。仿真结果表明，我们的解决方案在不同的老化条件和不同的负载配置的有效性。最后，我们反思我们提出的诊断方法，通过评估其鲁棒性，并将其与现有的替代品进行比较。
英文摘要：
Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) as sensors for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML) based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize state-of-the-art reflectometry methods within the PLMs to extract beneficial features for effective performance of our proposed ML solution. Simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.
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。
