Online Monitoring of Smart Grid Infrastructures Using Power Line Communications

智能电网的宗旨之一是实现对系统参数的普遍和实时监测和控制，以集成分布式发电，提高供电的可靠性和效率。除了运行网格功能之外，实时监控还可以扩展到网格资产。这包括传输线和地下电缆，以及变压器和开关设备等设备。资产监测通常需要安装专用的传感设备，并与处理传感器信息的控制中心建立通信链路。资产监测还涉及大量的人为干预，通常仅在故障发生后或在要求资产退出运行的维护过程中才进行。在本研究方案中，我们将研究和开发利用电力线通信(PLC)对电网资产进行在线监测。基本的范例是，PLC信号的主要目的是携带数据，通过网格传输，从而能够对其属性进行推断。这适用于PLC信号通过其传播的传输线和电缆，并延伸到影响信号传播并可能在异常情况下生成不同信号签名的连接资产。拟议方法的主要预期好处是，监测将实时和连续地进行，而不会造成任何服务中断和人为干预，即PLC能够实现真正的在线监测，这是今天所不具备的。它还意味着，可以在故障发生之前检测到电缆健康状况的恶化，并可以安排组件的预防措施和有针对性的维护。这将为系统运营商带来显著的经济效益，并为消费者提供服务水平的提高。建议的研究将集中在电缆老化和故障监测方面，作为一个具体而困难的应用案例。主要目标是开发一个由PLC传输前端和信息处理后端组成的PLC使能电网监测框架，这两个前端共同实现对电缆劣化的自主分类和严重程度估计。该研究计划将彻底解决实现这一目标的主要挑战。包括基于物理原理的退化机理建模，从PLC信号中提取指示基础设施状态的特征，结合来自多个测量的特征和集成用于自动推理的机器学习方法，研究所提出的方法与商用PLC产品的可行性，以及模型和方法的实验验证。这项研究将进一步扩展到其他电网基础设施监测任务，并将根据对现有监测方法的最佳补充进行评估。

One of the tenets of smart power grids is the availability of pervasive and real-time monitoring and control of system parameters for the integration of distributed generation and improved reliability and efficiency of electricity supply. Beyond the operational grid features, real-time monitoring and control can be extended to grid assets. This includes transmission lines and underground cables, and equipment such as transformers and switchgear. Asset monitoring typically requires the implementation of dedicated sensing devices and a communication link to a control centre at which the sensor information is processed. Asset monitoring also involves significant human intervention and often is done only after a fault occurred or in a maintenance that requires the asset to be taken out of operation. In this research proposal, we will investigate and develop the use of power line communication (PLC) for online monitoring of grid assets. The underlying paradigm is that the PLC signals, whose primary purpose is to carry data, travel through the grid and thus enable inference about its properties. This applies to transmission lines and cables, through which PLC signals propagate, and extends to connected assets which affect the signal propagation and may generate distinct signal signatures in case of anomalies. The main expected benefit of the proposed approach is that monitoring would occur real-time and continuously without any service interruption and human intervention, i.e., PLC enables true online monitoring, which is not available today. It also means that degradation of e.g. cable health can be detected before a fault occurs and preventative measures and targeted maintenance of components can be scheduled. This will provide significant economic benefits for system operators and service-level improvement for consumers. The proposed research will focus on cable aging and fault monitoring as a concrete and difficult application case. The main objective is to develop a framework for PLC -enabled grid monitoring that consists of a PLC transmission front--end and an information processing back--end, which together achieve autonomous classification and severity estimation for cable degradation. The research program will thoroughly address the main challenges in achieving this objective. These include modelling of degradation mechanisms based on physical principles, extraction of features from PLC signals which are indicative of infrastructure status, combining of features from many measurements and integration of machine learning methods for automated inference, studying the feasibility of the proposed methods with commercially available PLC products, and experimental verification of models and methods. The research will further be extended to other grid-infrastructure monitoring tasks and will also be evaluated in terms of best complementing the existing monitoring methods.