Forecasting Power Quality Indicators Using Artificial Neural Networks in the Load Connection Process in Electric Power Systems

Forecasting Power Quality Indicators Using Artificial Neural Networks in the Load Connection Process in Electric Power 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 Forecasting Power Quality Indicators Using Artificial Neural Networks in the Load Connection Process in Electric Power Systems Maria F. de Oliveira, André B. Farias, Gilcélia R. de Souza, Wesley J. de Paula, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9261210/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 The integration of nonlinear loads modifies current waveforms and contributes to harmonic distortion, voltage deviations, and phase unbalance in power grid environments, increasing the complexity of Power Quality (PQ) assessment during planning stages. Existing approaches typically rely on detailed system modeling and repeated simulations, which may become impractical when multiple connection scenarios must be evaluated. This work proposes a predictive framework based on a reduced set of physically interpretable descriptors, combining indices derived from the Conservative Power Theory (CPT) with the Short-Circuit Ratio (SCR) to jointly represent load behavior and power grid conditions at the point of connection. These descriptors are used as inputs to an Artificial Neural Network (ANN) trained to approximate the relationship between electrical operating conditions and PQ indicators. The training dataset is generated from simulations of an IEEE benchmark system, covering multiple load configurations and operating scenarios. The model is used to estimate quantities such as voltage Total Harmonic Distortion and CPT-based performance indices. The obtained results show that the adopted descriptor space captures the main electrical behaviors governing PQ response across the analyzed conditions. Lower approximation errors are observed in scenarios dominated by a single physical mechanism, while increased deviations occur in cases involving combined distortion and unbalance effects, reflecting the underlying coupling between load characteristics and power grid properties. Within the considered domain, the proposed approach provides a physically grounded formulation for the preliminary evaluation of disturbing load integration, enabling the estimation of PQ indicators from a compact set of electrical descriptors without requiring repeated full-system simulations. Power quality Conservative Power Theory Short-circuit ratio Harmonic distortion Nonlinear loads Reduced-order modeling Neural networks. 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. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9261210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620574442,"identity":"cd379e62-c4f7-4c75-a438-26ae6417604f","order_by":0,"name":"Maria F. de Oliveira","email":"","orcid":"","institution":"Federal University of São João\ndel-Rei (UFSJ)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"F.","lastName":"de Oliveira","suffix":""},{"id":620574443,"identity":"a8c1cc0e-1783-4628-b5ca-6b1f66d23818","order_by":1,"name":"André B. 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