Sparsity-Based Error Detection in DC Power Flow State Estimation

TL;DR

This paper introduces a sparsity-based method using $l_1$-norm minimization to accurately detect measurement errors in DC power flow state estimation, even with large noise magnitudes, demonstrated on IEEE test systems.

Contribution

It proposes a novel sparse vector recovery approach leveraging the impedance matrix's singularity for precise error detection in DC power flow estimation.

Findings

Exact measurement error detection demonstrated on IEEE 118-bus system.

Robustness to large-magnitude noise shown in simulations.

Effective sparse error identification in test systems.

Abstract

This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses $l_1$-norm minimization for state estimation. This approach can provably compute the measurement errors exactly, and its performance is robust to the arbitrary magnitudes of the measurement errors. Hence, the proposed approach can detect the noisy elements if the measurements are contaminated with additive white Gaussian noise plus sparse noise with large magnitude. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems.