22,236 research outputs found
Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
Limited presence of nodal and line meters in distribution grids hinders their
optimal operation and participation in real-time markets. In particular lack of
real-time information on the grid topology and infrequently calibrated line
parameters (impedances) adversely affect the accuracy of any operational power
flow control. This paper suggests a novel algorithm for learning the topology
of distribution grid and estimating impedances of the operational lines with
minimal observational requirements - it provably reconstructs topology and
impedances using voltage and injection measured only at the terminal (end-user)
nodes of the distribution grid. All other (intermediate) nodes in the network
may be unobserved/hidden. Furthermore no additional input (e.g., number of grid
nodes, historical information on injections at hidden nodes) is needed for the
learning to succeed. Performance of the algorithm is illustrated in numerical
experiments on the IEEE and custom power distribution models
Data-driven Localization and Estimation of Disturbance in the Interconnected Power System
Identifying the location of a disturbance and its magnitude is an important
component for stable operation of power systems. We study the problem of
localizing and estimating a disturbance in the interconnected power system. We
take a model-free approach to this problem by using frequency data from
generators. Specifically, we develop a logistic regression based method for
localization and a linear regression based method for estimation of the
magnitude of disturbance. Our model-free approach does not require the
knowledge of system parameters such as inertia constants and topology, and is
shown to achieve highly accurate localization and estimation performance even
in the presence of measurement noise and missing data
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