Resolving Steam Turbine Casing Thermal Management Challenges with a Dual Attentive Bi-GRU Soft Sensor for Transient Operation
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Kruk-Gotzman Sylwia, Bzymek Grzegorz, Kania Konrad |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 22 |
| Wolumen/Tom: | 18 |
| Numer artykułu: | 5213 |
| Strony: | 1 - 23 |
| Impact Factor: | 3,2 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | Some parts of the research were carried out as part of the implementation doctorate project within the Program of the Polish Ministry of Science and Higher Education entitled “Doktorat wdrożeniowy” DWD/6/0443/2022. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 18 listopada 2025 |
| Abstrakty: | angielski |
| This study introduces a novel dual-model deep learning framework based on Bidirectional Gated Recurrent Units (Bi-GRUs) with the Attention Mechanism to predict intermediate-pressure (IP) turbine casing temperatures in a 370 MW coal-fired power plant under varying operational regimes, including startup, shutdown, and load-following conditions. Accurate temperature prediction is critical, as thermal gradients induce significant stresses in the turbine casing, potentially causing fatigue crack initiation. To mitigate sensor failures, which lead to costly downtime in power generation systems, the proposed soft sensor leverages an extensive dataset collected over one year from Unit 4 of the Opole Power Plant. The dataset is partitioned into shutdown and active regimes to capture distinct thermal dynamics, enhancing model adaptability. The framework employs advanced preprocessing techniques and state detection heuristics to improve prediction robustness. Experimental results show that the dual-model approach outperforms traditional machine learning models (Random Forest Regressor, XGBoost) and single-model deep learning baselines (LSTM, Single Attentive Bi-GRU), achieving a mean squared error (MSE) of 2.97 °C and a mean absolute error (MAE) of 1.07 °C on the test set, while also maintaining low prediction latency suitable for real-time applications. This superior performance stems from a tailored architecture, optimized via Hyperband tuning and a strategic focus on distinct operational regimes. This work advances soft sensing in power systems and provides a practical, real-time solution for stress monitoring and control, particularly as coal plants in Poland face increased cycling demands due to the growth of renewable energy sources, rising from 7% in 2010 to 25% by 2025. The approach holds potential for broader application in industrial settings requiring robust temperature prediction under variable conditions. |
