Grant: Discovery Grants Program - Individual

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.