EAO-BiTCN-BiGRU-Based Overvoltage Prediction for Variable Frequency Motor Ends in Oil Drilling Platforms

EAO-BiTCN-BiGRU-Based Overvoltage Prediction for Variable Frequency Motor Ends in Oil Drilling Platforms | 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 EAO-BiTCN-BiGRU-Based Overvoltage Prediction for Variable Frequency Motor Ends in Oil Drilling Platforms Junjie Song, Zhenzhong Zhang, Leijie Jiang, Rong Yang, Yapeng Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7928730/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 As the power source for oil drilling operations, the operational reliability of variable frequency motors directly affects drilling stability. Insulation faults, as potential hidden hazards in motor operation, are characterized by strong concealment and slow progression, making them a critical factor influencing long-term stable motor operation. Therefore, research on insulation fault diagnosis in variable frequency motors is of significant importance for ensuring efficient drilling production. This study addresses the rapid insulation degradation of the main drive motor on the "Exploration No. 8" drilling platform caused by voltage spikes. We propose a hybrid prediction model based on an Improved Aquila Optimizer (EAO)-optimized Bidirectional Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (BiTCN-BiGRU). By analyzing the generation mechanism of overvoltage and optimizing hyperparameters using EAO, the model leverages BiTCN to extract multi-scale local features and BiGRU to model bidirectional temporal dependencies, aiming to accurately predict the amplitude and trend of motor terminal overvoltage and provide reliable data support for insulation degradation analysis.Experimental results demonstrate that the proposed model significantly outperforms traditional neural network models in prediction accuracy, effectively capturing the temporal characteristics of voltage variations and achieving high-precision prediction of motor terminal overvoltage. variable frequency motors Insulation faults overvoltage neural networks predictive models 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-7928730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549794077,"identity":"d91dc632-d37f-481a-8589-c469c6d36a3b","order_by":0,"name":"Junjie 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