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Observability and bad data analysis in the context of the recently developed blocked sparse approach are discussed. The factorization-based observability analysis is extended to the new method. Important statistical derivations involving the blocked augmented matrices are presented. The computational aspects of performing bad data analysis as well as guidelines for computing residual variances and sensitivity matrices, are discussed. An efficient implementation of a bad data analysis algorithm based on normalized residuals and nonlinear residual computations is described.

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