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Found 6 papers, 0 papers with code
Power Grid Parameter Estimation Without Phase Measurements: Theory and Empirical Validation
The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot.
Regularization for distributionally robust state estimation and prediction
We provide a direct method using samples of the noise to create a moving horizon observer for linear time-varying and nonlinear systems, which is optimal under the empirical noise distribution.
Minimal regret state estimation of time-varying systems
Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice.
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
Not measuring the voltage phase only adds 30\% of error to the admittance matrix estimate in realistic conditions.
Robust online joint state/input/parameter estimation of linear systems
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion.
Bayesian Error-in-Variables Models for the Identification of Power Networks
The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork.
