Approximations for Optimal Experimental Design in Power System Parameter Estimation

TL;DR

This paper introduces three numerical approximation methods to make optimal experiment design computationally feasible for power system parameter estimation, especially in larger grids where exact solutions are intractable.

Contribution

It proposes novel approximation techniques that improve computational efficiency of OED in power systems, validated on small case studies.

Findings

Approximation methods significantly reduce computation time.

Methods achieve comparable accuracy to exact OED.

Validated on 5-bus and 14-bus power system cases.

Abstract

This paper is about computationally tractable methods for power system parameter estimation and Optimal Experiment Design (OED). Here, the main motivation is that OED has the potential to significantly increase the accuracy of power system parameter estimates, for example, if only a few batches of data are available. The problem is, however, that solving the exact OED problem for larger power grids turns out to be computationally expensive and, in many cases, even computationally intractable. Therefore, the present paper proposes three numerical approximation techniques, which increase the computational tractability of OED for power systems. These approximation techniques are bench-marked on a 5-bus and a 14-bus case studies.