Multi-Variable Wind Power Anomaly Correction using Physics-Aware Transformer with Adaptive Temporal Encoding

Multi-Variable Wind Power Anomaly Correction using Physics-Aware Transformer with Adaptive Temporal Encoding | 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 Multi-Variable Wind Power Anomaly Correction using Physics-Aware Transformer with Adaptive Temporal Encoding Tang yanjuan, Luo zuyun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7487232/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Electrical Engineering → Version 1 posted 8 You are reading this latest preprint version Abstract Wind power has emerged as an essential element of worldwide renewable energy systems; yet, the substantial aberrant data within wind turbine SCADA systems presents considerable hurdles to grid stability and operational efficiency, adversely impacting power forecasting precision and grid scheduling dependability. This research presents a multi-variable wind power anomaly correction technique utilizing a Physics-Aware Transformer. The method develops an adaptive temporal encoding mechanism to identify multi-scale periodic patterns and formulates a physics-informed composite loss function that incorporates reconstruction accuracy, temporal smoothness, and capacity constraints to ensure that correction outcomes adhere to the physical laws governing wind power systems. Validation using actual SCADA data from a 280MW offshore wind farm in Fujian indicates that the suggested method realizes a 3.04% enhancement in MAE and a 0.68% enhancement in RMSE relative to the optimal baseline method, XGBoost, in anomaly correction tasks. LSTM prediction models trained with corrected data provide a 10.2% improvement in MAE and a 20.8% enhancement in R² for 24-step long-term forecasting compared to the deletion technique, thereby greatly enhancing the accuracy and stability of wind power forecasting. The research findings offer a viable technical method for managing wind power data quality and improving forecasting accuracy. Wind power Anomaly correction Transformer Physics-aware Multi-variable Temporal encoding Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Electrical Engineering → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 12 Oct, 2025 Editor assigned by journal 30 Aug, 2025 Submission checks completed at journal 30 Aug, 2025 First submitted to journal 29 Aug, 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. 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