Szczegóły publikacji
Opis bibliograficzny
Control-driven SCADA framework for wind turbine degradation prediction and model selection / Anna JAROSZ-KOZYRO // W: 27th International Carpathian Control Conference (ICCC) [Dokument elektroniczny] : 1-3 June 2026, Szilvásvárad, Hungary : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2026. — Print on Demand(PoD) ISBN: 979-8-3195-3321-0. — e-ISBN: 979-8-3195-3320-3. — S. 228–235. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 234–235, Abstr. — Publikacja dostępna online od: 2026-07-07
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169785 |
|---|---|
| Data dodania do BaDAP | 2026-10-02 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCC71363.2026.11593388 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
This study emphasizes the integration of advanced control strategies to enhance wind turbine performance and reliability and conducts a comparative analysis of models for predicting wind turbine degradation using SCADA data. The models evaluated include linear regression, quadratic regression, Weibull regression, Poisson regression, generalized linear models, hierarchical generalized linear models, random forest, and Bayesian MCMC. The evaluation leverages likelihood-based criteria such as Akaike Information Criterion, Bayesian Information Criterion, and Widely Applicable Information Criterion. Proactive maintenance scheduling, real-time monitoring, and system-level optimization are explored as key approaches to mitigate degradation and improve operational resilience. Emerging trends in control co-design and adaptive algorithms further demonstrate the potential for integrating predictive models with dynamic control systems to achieve fully autonomous wind turbine operations. The study culminates in a technical mapping that aligns model-specific statistical strengths (e.g., minimum BIC for Quadratic models) with prescriptive control actions, providing a robust foundation for autonomous wind farm operations.
