Optimal PMU Placement for Kalman Filtering of DAE Power System Models

TL;DR

This paper presents a new method for optimally placing sensors in power systems to improve state estimation accuracy by minimizing the Kalman filter covariance, using a mixed-integer semidefinite programming approach.

Contribution

It introduces a novel optimization framework for sensor placement in DAE power system models, outperforming greedy algorithms in accuracy and efficiency.

Findings

The proposed method effectively minimizes estimation error.

Benchmarking shows improved sensor placement over greedy algorithms.

The approach is computationally feasible with existing solvers.

Abstract

Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power systems modeled by differential-algebraic equations. The method identifies optimal sensor locations by minimizing the steady-state covariance matrix of the Kalman filter, thus minimizing the error of joint differential and algebraic state estimation. The problem is reformulated as a mixed-integer semidefinite program and effectively solved using off-the-shelf numerical solvers. Numerical results demonstrate the merits of the proposed approach by benchmarking its performance in phasor measurement unit placement in comparison to greedy algorithms.