13,419 research outputs found
Sparsity-Based Error Detection in DC Power Flow State Estimation
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 -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
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Diagnostic Applications for Micro-Synchrophasor Measurements
This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems
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