Abstract
This letter shows that the sparse state recovery optimization method is equivalent to the well-known Huber M-estimator, and then justifies its robustness to bad data. We derive the total influence functions of the Huber M-estimator and the generalized maximum-likelihood (GM)-estimator, and give a formal proof that the Huber M-estimator is vulnerable to bad leverage points while the GM-estimator can handle them. Numerical results carried out on various IEEE systems validate our theoretical results.
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