Hydrodynamic feature extraction and intelligent identification of flow regimes in vaneless space of a pump turbine using improved empirical wavelet transform and Bayesian optimized convolutional neural network

Xianghao Zheng, Hao Li, Suqi Zhang, Yuning Zhang, Jinwei Li, Yuning Zhang and Weiqiang Zhao

Energy, 2023, vol. 282, issue C

Abstract: Hydrodynamic feature extraction of pressure pulsation signals (PPSs) and intelligent identification of flow regimes in vaneless space (VAS) of a pump turbine (PT) are crucial to the safe and stable operations of the pumped storage power station. In this work, the scheme based on an improved empirical wavelet transform (IEWT), energy feature vector (EFV) and Bayesian optimized convolutional neural network (BOCNN) is innovatively proposed. Firstly, the IEWT is proposed by introducing the least square method and mathematical morphology to improve the decomposition shortcomings of existing methods. The phenomenon of mode aliasing is eliminated and the influence of background noise is greatly reduced, as verified by both simulated and measured PPSs. Secondly, based on the IEWT, several significant mode components are obtained, and the energy feature indexes of them are calculated to extract the hydrodynamic feature information and construct the EFVs that can accurately reflect the features of different flow regimes in the VAS. Then, the BO algorithm is adopted to optimize the important hyperparameters of CNN, and the intelligent identification model of BOCNN is constructed and trained to identify four typical types of flow regimes in the VAS. Finally, the average identification accuracy of the proposed IEWT-EFV-BOCNN scheme can reach 99.15%, which is much higher than traditional schemes, illustrating that the proposed scheme has significant engineering application value.

Keywords: Pumped hydro energy storage; Pump turbine; Vaneless space; Pressure pulsation; Improved empirical wavelet transform; Intelligent identification (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223020996

DOI: 10.1016/j.energy.2023.128705

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