Photo by heeybooy from unsplash
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… Click to show full 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.
Keywords:
estimator;
maximum likelihood;
generalized maximum;
sparse state;
state recovery;
likelihood estimator
Journal Title: IEEE Transactions on Power Systems
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!
Journal Title: IEEE Transactions on Power Systems
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!