Location
Hilton Waikoloa Village, Hawaii
Event Website
http://hicss.hawaii.edu/
Start Date
1-3-2018
End Date
1-6-2018
Description
The power systems state estimation problem computes the set of complex voltage phasors given quadratic measurements using nonlinear least squares (NLS). This is a nonconvex optimization problem, so even in the absence of measurement errors, local search algorithms like Newton / Gauss-Newton can become "stuck" at local minima, which correspond to nonsensical estimations. In this paper, we observe that local minima cease to be an issue as redundant measurements are added. Posing state estimation as an instance of the quadratic recovery problem, we derive a bound for the distance between the true solution and the nearest spurious local minimum. We use the bound to show that critical points of the nonconvex least squares objective become increasing rare and far-away from the true solution with the addition of redundant information.
Spurious Critical Points in Power System State Estimation
Hilton Waikoloa Village, Hawaii
The power systems state estimation problem computes the set of complex voltage phasors given quadratic measurements using nonlinear least squares (NLS). This is a nonconvex optimization problem, so even in the absence of measurement errors, local search algorithms like Newton / Gauss-Newton can become "stuck" at local minima, which correspond to nonsensical estimations. In this paper, we observe that local minima cease to be an issue as redundant measurements are added. Posing state estimation as an instance of the quadratic recovery problem, we derive a bound for the distance between the true solution and the nearest spurious local minimum. We use the bound to show that critical points of the nonconvex least squares objective become increasing rare and far-away from the true solution with the addition of redundant information.
https://aisel.aisnet.org/hicss-51/es/markets/4