Robust State Estimators Based on Maximum Correntropy and Weighted Least Median of Squares Criteria: A Comparative Study

Robust State Estimators Based on Maximum Correntropy and Weighted Least Median of Squares Criteria: A Comparative Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robust State Estimators Based on Maximum Correntropy and Weighted Least Median of Squares Criteria: A Comparative Study Jose Alejandro Encinas Riveros, Eduardo Augusto Pereira Gomes, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429660/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract This paper presents a comparative study of statistically robust state estimators for power system state estimation. Estimators based on the Maximum Correntropy Criterion (MCC) are evaluated using different Parzen window adjustment strategies, including the proposed Adaptive Kernel Spreading (AKS) method and two kernel reduction approaches, namely Safety-Margin Kernel Reduction (SMKR) and Hessian-Bounded Kernel Reduction (HBKR). These methods are compared with the Weighted Least Median of Squares (WLMS) estimator and the Weighted Least Squares estimator with residual analysis (WLSr). Computational simulations are conducted on the IEEE 14-bus and IEEE 30-bus test systems under Gaussian and non-Gaussian noise conditions, as well as in the presence of gross errors (GEs). The results reveal significant performance differences among the estimators, particularly under non-Gaussian noise and reduced measurement redundancy. MCC-based estimators demonstrate high robustness to GEs without requiring measurement removal, while the proposed AKS method achieves performance comparable to WLSr under Gaussian noise and superior performance in non-Gaussian scenarios with limited redundancy. These findings provide practical insights into the selection and application of robust state estimation techniques in power systems. State Estimation Robust State Estimation Maximum Correntropy Criterion Parzen Windows Adaptive Kernel Spreading Weighted Least Median of Squares Non-Gaussian Noise. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 30 Dec, 2025 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 22 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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