A review of OMA parameter identification for hydro-turbine unit: Challenges in condition monitoring

Juan Duan, Zeyu Peng, Luyang Chen and Yun Zeng

Renewable and Sustainable Energy Reviews, 2025, vol. 217, issue C

Abstract: Hydropower plays a crucial role in the global transition to clean energy. Currently, hydropower is not only responsible for power generation but also fulfills essential functions such as peak load balancing and frequency stabilization for intermittent clean energy systems. The hydro-turbine unit has serious vibration operating in off-design condition. Long-term vibration would lead to the fatigue of components and affect the safe and stable operation of unit. The condition monitoring of the hydro-turbine unit enables real-time assessment of the health status and potential damage to its components. The Operational Modal Analysis (OMA) method is an effective method for condition monitoring due to the modal parameters are sensitive to the damage. However, most of the current OMA methods to study the condition monitoring of structures are by vibration response signal under single-source excitation. It is a challenge to directly apply the OMA method for condition monitoring of hydro-turbine unit under multi-source excitation. In this review, the application of OMA method used to Structural Health Monitoring (SHM) are introduced. The advantages and limitations of OMA for hydro-turbine unit are shown. Meanwhile, the potential application direction of OMA method for condition monitoring of different parts of hydro-turbine unit is also discussed. Then, the challenging issues in applying the OMA method to the condition monitoring of hydro-turbine unit under multi-source excitation are analyzed. Finally, some improved OMA methods applicable to condition monitoring of hydro-turbine under multi-source excitation are prospected. This review could provide a way for extending the OMA method to hydro-turbine condition monitoring.

Keywords: Operational modal analysis; Hydro-turbine unit; Parameter identification; Condition monitoring; Multi-source excitation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.115724

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