Graduate Thesis Or Dissertation
 

Identifiability-Aware Joint Sparse Error Correction For Robust State Estimation

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8p58pm526

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  • We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. The identifiability-aware approach is proposed to leverage common characteristics of fundamentally identifiable gross errors to enhance error correction performance. First, we derive a necessary rank condition that the sparsity pattern of any identifiable gross error should satisfy and {show that the condition is tight.} Second, we present an identifiability-aware algorithm for joint sparse error correction wherein we exploit the aforementioned identifiability condition to improve the accuracy of gross error localization. Furthermore, we provide an iterative solver for the associated nonlinear sparse optimization problem with a convergence proof. We demonstrate the efficacy of the proposed approach by applying it to power system nonlinear state estimation of IEEE 14-bus and 118-bus networks.
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  • Pending Publication
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  • 2020-07-02 to 2022-08-03

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