| Summary: | In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>E</mi><mi>E</mi><mi>E</mi></mrow></semantics></math></inline-formula> 14 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>E</mi><mi>E</mi><mi>E</mi></mrow></semantics></math></inline-formula> 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.
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