Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning
Abstract Hydropower systems face significant challenges in load control and fault detection due to their complex operational dynamics. This study presents an innovative framework combining Digital Twin technology with Deep Learning to enhance fault detection, optimize operations, and improve system...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98235-1 |
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| author | Jun Tan Raoof Mohammed Radhi Kimia Shirini Sina Samadi Gharehveran Zamen Parisooz Mohsen Khosravi Hossein Azarinfar |
| author_facet | Jun Tan Raoof Mohammed Radhi Kimia Shirini Sina Samadi Gharehveran Zamen Parisooz Mohsen Khosravi Hossein Azarinfar |
| author_sort | Jun Tan |
| collection | DOAJ |
| description | Abstract Hydropower systems face significant challenges in load control and fault detection due to their complex operational dynamics. This study presents an innovative framework combining Digital Twin technology with Deep Learning to enhance fault detection, optimize operations, and improve system resilience. We developed a hybrid approach integrating a Digital Twin model of the hydropower system with advanced Deep Learning algorithms for real-time monitoring and predictive analysis. The proposed framework was evaluated through extensive simulations in a MATLAB environment, where it demonstrated remarkable improvements in system performance. The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. Our results show that the proposed method achieved a 12.14% reduction in fault detection time compared to traditional methods. Furthermore, the optimization of operational parameters led to a 8.97% increase in overall system efficiency and a 5.49% decrease in maintenance costs. In terms of fault detection accuracy, the Deep Learning-enhanced Digital Twin system achieved an 72% accuracy rate, significantly higher than the 65% accuracy observed with conventional techniques. The improved model not only enhanced fault detection but also contributed to a 8.03% reduction in energy loss and a 14.07% increase in power generation reliability. Overall, this research demonstrates that the integration of Digital Twins and Deep Learning provides a powerful, data-driven approach to optimizing hydropower systems. The proposed method offers substantial benefits in terms of operational efficiency, fault detection accuracy, and cost savings, positioning it as a significant advancement in the field. |
| format | Article |
| id | doaj-art-e3be0a41f090409ebdade4df9c944b2e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e3be0a41f090409ebdade4df9c944b2e2025-08-20T03:09:35ZengNature PortfolioScientific Reports2045-23222025-05-0115112810.1038/s41598-025-98235-1Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learningJun Tan0Raoof Mohammed Radhi1Kimia Shirini2Sina Samadi Gharehveran3Zamen Parisooz4Mohsen Khosravi5Hossein Azarinfar6School of Computer Science and Engineering, Hunan University of Information TechnologyAir Conditioning Engineering Department, College of Engineering, University of Warith Al-AnbiyaaFaculty of Electrical and Computer Engineering, Tabriz UniversityFaculty of Electrical and Computer Engineering, Tabriz UniversityIslamic Azad UniversityFaculty of Computer and Electrical Engineering, University of GonabadFaculty of Computer and Electrical Engineering, University of GonabadAbstract Hydropower systems face significant challenges in load control and fault detection due to their complex operational dynamics. This study presents an innovative framework combining Digital Twin technology with Deep Learning to enhance fault detection, optimize operations, and improve system resilience. We developed a hybrid approach integrating a Digital Twin model of the hydropower system with advanced Deep Learning algorithms for real-time monitoring and predictive analysis. The proposed framework was evaluated through extensive simulations in a MATLAB environment, where it demonstrated remarkable improvements in system performance. The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. Our results show that the proposed method achieved a 12.14% reduction in fault detection time compared to traditional methods. Furthermore, the optimization of operational parameters led to a 8.97% increase in overall system efficiency and a 5.49% decrease in maintenance costs. In terms of fault detection accuracy, the Deep Learning-enhanced Digital Twin system achieved an 72% accuracy rate, significantly higher than the 65% accuracy observed with conventional techniques. The improved model not only enhanced fault detection but also contributed to a 8.03% reduction in energy loss and a 14.07% increase in power generation reliability. Overall, this research demonstrates that the integration of Digital Twins and Deep Learning provides a powerful, data-driven approach to optimizing hydropower systems. The proposed method offers substantial benefits in terms of operational efficiency, fault detection accuracy, and cost savings, positioning it as a significant advancement in the field.https://doi.org/10.1038/s41598-025-98235-1Hydropower systemsDigital twin technologyDeep learningFault detectionSystem optimizationReal-Time monitoring |
| spellingShingle | Jun Tan Raoof Mohammed Radhi Kimia Shirini Sina Samadi Gharehveran Zamen Parisooz Mohsen Khosravi Hossein Azarinfar Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning Scientific Reports Hydropower systems Digital twin technology Deep learning Fault detection System optimization Real-Time monitoring |
| title | Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| title_full | Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| title_fullStr | Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| title_full_unstemmed | Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| title_short | Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| title_sort | innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning |
| topic | Hydropower systems Digital twin technology Deep learning Fault detection System optimization Real-Time monitoring |
| url | https://doi.org/10.1038/s41598-025-98235-1 |
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