Underdetermined Library-aided Impedance Estimation with Terminal Smart Meter Data
This work addresses impedance estimation for power grid operators using terminal smart meter data, offering a method that is incremental by refining existing approaches with a library of cable types.
The paper tackles the problem of impedance identification in power networks using smart meter data, which lacks global phase alignment and internal node observations, by proposing a method that handles ambiguous topologies and data to output a collection of data-compatible impedance assignments, achieving high identification performance on a benchmark case with low-size datasets.
Smart meters provide relevant information for impedance identification, but they lack global phase alignment and internal network nodes are often unobserved. A few methods for this setting were developed, but they have requirements on data correlation and/or network topology. In this paper, we offer a unifying view of data- and structure-driven identifiability issues, and use this groundwork to propose a method for underdetermined impedance identification. The method can handle intrinsically ambiguous topologies and data; its output is not forcedly a single estimate, but instead a collection of data-compatible impedance assignments. It uses a library of plausible commercial cable types as a prior to refine the solutions, and we show how it can support topology identification workflows built around known georeferenced joints without degree guarantees. The method depends on a small number of non-sensitive parameters and achieves high identification performance on a sizeable benchmark case even with low-size injection/voltage datasets. We identify key steps that can be accelerated via GPU-based parallelization. Finally, we assess the tolerance of the identification to noisy input.