Power System State Estimation by Phase Synchronization and Eigenvectors
Iven Guzel, Richard Y. Zhang

TL;DR
This paper introduces a spectral initialization method for power system state estimation that guarantees global optimality and improves accuracy of voltage angle estimation using phase synchronization and eigenvector techniques.
Contribution
It proposes a novel spectral initialization approach for power system state estimation that provides high-quality initial guesses and correctness guarantees, enhancing existing iterative methods.
Findings
Spectral initialization yields near-perfect angle estimates from noisy measurements.
One Gauss-Newton iteration suffices to guarantee global optimality.
Method performs well even with moderate voltage magnitude errors.
Abstract
To estimate accurate voltage phasors from inaccurate voltage magnitude and complex power measurements, the standard approach is to iteratively refine a good initial guess using the Gauss--Newton method. But the nonconvexity of the estimation makes the Gauss--Newton method sensitive to its initial guess, so human intervention is needed to detect convergence to plausible but ultimately spurious estimates. This paper makes a novel connection between the angle estimation subproblem and phase synchronization to yield two key benefits: (1) an exceptionally high quality initial guess over the angles, known as a \emph{spectral initialization}; (2) a correctness guarantee for the estimated angles, known as a \emph{global optimality certificate}. These are formulated as sparse eigenvalue-eigenvector problems, which we efficiently compute in time comparable to a few Gauss-Newton iterations. Our…
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Taxonomy
TopicsNonlinear Dynamics and Pattern Formation · Power System Optimization and Stability · Power Systems and Renewable Energy
