Grid Surveillance and Diagnostics using Power Line Communications

With an aging power distribution infrastructure, it becomes increasingly important for the next generation smart grid to self-monitor its transmission lines. In this paper, we propose a road-map towards achieving a self-reliant grid surveillance using power line communications (PLC). To this end, we exploit the principle that cable faults or degradations manifest themselves as changes in the PLC channel conditions. In particular, by monitoring the channel transfer functions that are already computed in legacy PLC receivers, we enable power line modems with intelligent grid sensing abilities to identify and assess cable anomalies using machine learning techniques. Through simulations, we show that our proposed monitoring and diagnostics solutions successfully empower power line modems to independently detect and predict the extent of water-tree degradations commonly seen in cross-linked polyethylene insulated power cables.