English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a new approach for identifying the measurement error inthe DC power flow state estimation problem. The proposed algorithm exploits thesingularity of the impedance matrix and the sparsity of the error vector byposing the DC power flow problem as a sparse vector recovery problem thatleverages the structure of the power system and uses $l_1$-norm minimizationfor state estimation. This approach can provably compute the measurement errorsexactly, and its performance is robust to the arbitrary magnitudes of themeasurement errors. Hence, the proposed approach can detect the noisy elementsif the measurements are contaminated with additive white Gaussian noise plussparse noise with large magnitude. The effectiveness of the proposedsparsity-based decomposition-DC power flow approach is demonstrated on the IEEE118-bus and 300-bus test systems.

Sparsity-Based Error Detection in DC Power Flow State Estimation