Priya Donti, Carnegie Mellon University

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With the rising penetration of distributed energy resources, distribution control and enabling techniques such as state estimation have become essential to system operation. However, traditional state estimation techniques have difficulty coping with the low-observability conditions often present on the distribution system due to the paucity of sensors and heterogeneity of measurements. To address these limitations, we propose a state estimation algorithm that employs matrix completion (a tool for estimating missing values in low-rank matrices) augmented with noise-resilient power flow constraints. This method operates under low-observability conditions where standard least-squares-based methods cannot operate and flexibly incorporates any network quantities measured in the field. We empirically evaluate our method on the IEEE 33- and 123-bus test systems and find that it provides near-perfect state estimation performance (within 1 percent mean absolute percent error) across many low-observability data availability regimes.

Abstract Author(s): Priya L. Donti, Yajing Liu, Andreas J. Schmitt, Andrey Bernstein, Rui Yang, Yingchen Zhang