Identification of Power System Dynamic Model Parameters using the Fisher Information Matrix

TL;DR

This paper introduces a scalable numerical method using the Fisher Information Matrix to efficiently estimate dynamic power system model parameters, addressing issues of unavailability and inaccuracy due to aging or market factors.

Contribution

It proposes a novel numerical approximation of the Fisher Information Matrix that is scalable to complex EMT models for dynamic parameter inference in power systems.

Findings

nFIM aligns with least-squares estimator variances for IEEE 9-bus models

Method is scalable to EMT models with high computational complexity

Case studies validate the effectiveness of the proposed approach

Abstract

The expected decrease in system inertia and frequency stability motivates the development and maintenance of dynamic system models by Transmission System Operators. However, some dynamic model parameters can be unavailable due to market unbundling, or inaccurate due to aging infrastructure, non-documented tuning of controllers, or other factors. In this paper, we propose the use of a numerical approximation of the Fisher Information Matrix (nFIM) for efficient inference of dynamic model parameters. Thanks to the proposed numerical implementation, the method is scalable to Electromagnetic Transient (EMT) models, which can quickly become computationally complex even for small study systems. Case studies show that the nFIM is coherent with parameter variances of single- and multi-parameter least-squares estimators when applied to an IEEE 9-bus dynamic model with artificial measurements.