DT-AOF: A Digital Twin–Driven Adaptive Optimization Framework for Power Infrastructure Monitoring under Uncertain Operating Conditions

DT-AOF: A Digital Twin–Driven Adaptive Optimization Framework for Power Infrastructure Monitoring under Uncertain Operating Conditions | 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 Article DT-AOF: A Digital Twin–Driven Adaptive Optimization Framework for Power Infrastructure Monitoring under Uncertain Operating Conditions Zhuyan Yin, Gaoshun Song, Chengfeng Zhou, Hongjun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9332277/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This paper presents DT-AOF, a decision-theoretic adaptive optimization framework designed to address dynamic optimization problems under uncertainty. The proposed method integrates three key components: explicit uncertainty modeling to capture stochastic system variations, an adaptive weighting mechanism to balance multiple competing objectives over time, and a physics-informed model to enforce structural consistency and improve solution stability. By jointly optimizing these components within a unified framework, DT-AOF achieves robust and efficient decision-making in non-stationary environments. Extensive experiments and ablation studies demonstrate that DT-AOF consistently outperforms deterministic baselines and partially constrained variants in terms of overall cost reduction and convergence behavior. The results further reveal that removing any individual component leads to noticeable performance degradation, highlighting the necessity of their synergistic integration. These findings indicate that DT-AOF provides a principled and effective solution for complex adaptive optimization tasks with inherent uncertainty. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Editor invited by journal 16 Apr, 2026 Submission checks completed at journal 12 Apr, 2026 First submitted to journal 12 Apr, 2026 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. 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