Wavelet data decomposition for (micro)grid phasor measurement unit systems | 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 Wavelet data decomposition for (micro)grid phasor measurement unit systems Spiros Chountasis, Dimitrios Karamanis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7738527/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Data from Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs) are getting more common in power system operation. These data are currently utilized for real-time operation monitoring and offline analysis. This paper presents an intercommunication technique for efficient synchro-measurement data analysis. Both on-line and off-line data exploration are employed on the proposed wavelet coefficient decomposition. It provides a new approach to the analysis and interpretation of collected PDC/PMU data in a semantically interoperable manner. Τhe wavelet decomposition and synchro-measurement analytics can also been extended into the domain of microgrids. In this context, wavelet decomposition provides resilience by improving the detection of disturbances even from sparse measurements. Statistical characteristics obtained from wavelet coefficients such as energy concentration at specific scales or entropy measures, can serve as reliable indicators of unusual behavior. The experimental evaluation and the results presented in this study demonstrate that wavelet-based decomposition can extract rich feature sets from synchrophasor data, providing reliable detection of discontinuities, oscillations, and noise components. The proposed technique from a data standpoint found to be more efficient, selective and depicted for the study of control applications. Synchrophasor Technology Phasor Measurement Unit Phasor Data Concentrator Critical Infrastructures Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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