Cavitation Feature Extraction Method of Hydraulic Turbine Based on NS-TEMD and Cross Fuzzy Entropy
DOI:
https://doi.org/10.13052/spee1048-5236.4434Keywords:
turbine cavitation, acoustic emission, NS-TEMD, state recognitionAbstract
As a key equipment for hydropower energy conversion, water turbines may encounter cavitation problems during operation, which will lead to equipment performance degradation, efficiency reduction, and even failure. Monitoring and accurately identifying the cavitation state of water turbines is of great significance for the safe operation and performance optimization of equipment. Therefore, the cavitation feature extraction method based on Nonuniformly sampled Trivariate EMD (NSTEMD) and cross fuzzy entropy is used in this paper to analyze the cavitation phenomenon of large Francis turbines combined with acoustic emission detection method. The specific areas of inlet edge cavitation, rim clearance cavitation and vortex cavitation in the draft pipe are monitored, and various cavitation conditions and different degrees of cavitation areas of the turbine are clearly identified. The research results show that the method proposed in this paper can better capture the cavitation characteristics in the monitoring noise compared with other algorithms. The calculation results reveal that under the same working head conditions, within a certain load range, the cavitation degree of the turbine will intensify as the output power increases.
Downloads
References
Yan X, Zhang F, Kan K, et al. Hydraulic instability of pump-turbine during fast pump-to-turbine transition under different control schemes: Changing guide vane pre-opening angles[J]. Energy Conversion and Management, 2025, 323 (PA): 119274–119274.
Wang X, Yang F, Huang W X, et al. Using bionic tubercles to control swirling flow instabilities of a hydraulic turbine during the load rejection process[J]. Energy, 2024, 311133354–133354.
Amromin E. Analysis of Scale Effects on Tide Turbine Cavitation[J]. Ocean Engineering, 2022, 264.
Su W, Zhao W, Binama M, et al. Experimental Francis Turbine Cavitation Performances of a Hydro-Energy Plant[J]. Sustainability, 2022, 14(6): 3263.
Zhou P, Zeng W, Zhang W, et al. Multi-cavitation states identification of a sewage pump using CEEMDAN and BOA-SVM[J]. Journal of Water Process Engineering, 2024, 61105299–.
Wang Z, Han H, Zhao W, et al. A refined modal decomposition method for cavitating flow based on state recognition[J]. Ocean Engineering, 2024, 297116972–.
Li Y, Cui L. A Novel Acoustic Method for Cavitation Identification of Propeller[J]. Journal of Marine Science and Engineering, 2022, 10(9): 1225.
He X, Song Y, Wu K, et al. Intelligent Identification of Cavitation State of Centrifugal Pump Based on Support Vector Machine[J]. Energies, 2022, 15(23): 8907.
Song Y, Zhang T, Liu Q, et al. Cavitation Identification Method of Centrifugal Pumps Based on Signal Demodulation and Efficient Net[J]. Arabian Journal for Science and Engineering, 2024, (prepublish): 1–15.
Wang J, Liu X, Liu B, et al. Cavitation state recognition for control valve using Alex Net-type neural networks with three-channel images transformed by time series[J]. Measurement Science and Technology, 2023, 34(5).
Cui D, Hu S, Zhang Y, et al. Cavitation State Identification of Centrifugal Pump Based on CEEMD-DRSN[J]. Nuclear Engineering and Technology, 2023, 55(4): 1507–1517.
Feng J, Zhang N, Zheng G, et al. Cavitation identification in a hydraulic bulb turbine based on vibration and pressure fluctuation measurements[J]. Mechanical Systems and Signal Processing, 2024, 208111042–.
Liu Z, Li X, Zou S, et al. Cavitation State Recognition Method of Hydraulic Turbine Based on Chaos Theory and CNN-OSVM[J]. Journal of Power Engineering, 2023, 43(11): 1454–1460.
Liu Z, Li X, Zou S, et al. Noise Reduction of Cavitation Acoustic Emission Signal and Chaotic Image Feature Extraction of Hydraulic Turbine[J]. Journal of Harbin Engineering University, 2023, 44(08): 1361–1367.
Zhang Y. Research on Cavitation State Recognition of Hydraulic Turbine Based on Modal Decomposition and Optimized XGBoost Algorithm[D]. Xi’an University of Technology, 2023.
Hemakom A, Ahrabian A, Looney D, et al. Nonuniformly sampled trivariate empirical mode decomposition[J]. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, 2015, 3691–3695.
Xie H, Zheng Y, Guo J, et al. Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series[J]. Information Sciences, 2010, 180(9): 1715–1724.
https://aeeeuropeenergy.com/
