A hybrid physics aware learning based transmission line resistance estimation algorithm for dynamic line rating application
The worldwide push towards a more intelligent, connected and reliable electric power delivery system has led to the propagation of a wide range of new technologies and ideas within the power grid infrastructure. Thus, the power grid is becoming more adaptable to changes and more reliable under distress. However, these benefits are only possible with vastly improved observability in the system. The traditional methods and technologies for grid monitoring were simply too slow and newer, faster and more accurate monitoring technologies became essential over the turn of the century. With the advancement of micro processing and communication technologies at an incredibly fast pace, this became possible in the form of smart monitoring devices. These devices include Intelligent Electronic Devices (IEDs), smart meters for homes and, at the transmission level, the use of Synchrophasor Measurement Units (PMUs). Over the past decade, transmission utilities were quick to adopt these PMU networks and they are now common among most major utilities. Compared to traditional monitoring systems, PMUs provide information at a much higher resolution and have the advantage of being time synchronized. The benefits of these networks are numerous, but they are not without certain drawbacks. PMU devices only report some basic system parameters from the field. While these are useful on their own, it is possible to use this data, in combination with other information, to extrapolate additional parameters about the grid. However, in this process, inherent errors present in PMU estimated data become an issue and renders the results of this extrapolated information unusable. In this work, of particular focus from these additional parameters is transmission line resistance. The fundamental cause of error will be investigated, and this knowledge will be applied to create a correction algorithm to output corrected transmission line resistance estimates that are more useful to utilities for a range of auxiliary applications such as dynamic line rating, determination of line sag, and conductor temperature estimation. This advancement would allow utilities to compound the economic benefits of their investment in PMU networks.