Online Monitoring of Smart Grid Infrastructures Using Power Line Communications

使用电力线通信在线监控智能电网基础设施

• 批准号: RGPIN-2019-03965
• 负责人: Lampe, Lutz
• 金额: $ 4.01万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752162
• 关键词: Online; Monitoring; Smart; Grid; Infrastructures

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.

智能电网的原则之一是为分布式发电的集成和提高电力供应的可靠性和效率提供无处不在的实时监测和控制系统参数。除了可操作的网格功能之外，实时监控和控制还可以扩展到网格资产。这包括输电线路和地下电缆，以及变压器和开关设备等设备。资产监控通常需要实现专用传感设备和与控制中心的通信链路，在控制中心处理传感器信息。资产监控还涉及大量的人为干预，通常只在发生故障或需要停止资产运行的维护过程中进行。在本研究计划中，我们将研究和开发电力线通信（PLC）用于在线监测电网资产的使用。潜在的范例是PLC信号，其主要目的是携带数据，通过网格传播，从而能够对其属性进行推断。这适用于传输PLC信号的传输线和电缆，并扩展到连接的资产，这些资产会影响信号的传播，并且在异常情况下可能会产生明显的信号特征。所提出的方法的主要预期好处是，监测将实时和连续地进行，没有任何服务中断和人为干预，也就是说，PLC实现了真正的在线监测，这是目前无法实现的。这也意味着可以在故障发生之前检测到电缆健康状况的退化，并可以计划预防措施和有针对性的组件维护。这将为系统运营商提供显著的经济效益，并为消费者提高服务水平。提出的研究将集中在电缆老化和故障监测作为一个具体的和困难的应用案例。主要目标是为PLC支持的电网监测开发一个框架，该框架由PLC传输前端和信息处理后端组成，它们共同实现电缆退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。这些包括基于物理原理的退化机制建模，从指示基础设施状态的PLC信号中提取特征，将许多测量的特征结合起来，并将机器学习方法集成到自动推理中，研究所提出方法与市售PLC产品的可行性，以及模型和方法的实验验证。该研究将进一步扩展到其他电网基础设施监测任务，并将对现有监测方法的最佳补充进行评估。