Affiliation:
1. School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China
2. Hebei Key Laboratory of Intelligent Water Conservancy, Hebei University of Engineering, Handan 056038, China
Abstract
To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.
Funder
National Natural Science Foundation of China
China Scholarship Council
Subject
Molecular Medicine,Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biotechnology
Reference43 articles.
1. Yang, B., Bo, Z., Yawu, Z., Xi, Z., and Yalan, J. (2021, January 22–24). The vibration trend prediction of hydropower units based on wavelet threshold denoising and bi-directional long short-term memory network. Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China.
2. Nonstationary Vibration Signal Analysis of a Hydroturbine Based on Adaptive Chirplet Decomposition;Feng;Struct. Health Monit.,2007
3. On the use of Vibrational Hill Charts for improved condition monitoring and diagnosis of hydraulic turbines;Zhao;Struct. Health Monit.,2022
4. Pressure fluctuation signal analysis of a hydraulic turbine based on variational mode decomposition;An;Proc. Inst. Mech. Eng. Part A J. Power Energy,2015
5. Analysis on vibration signal of hydropower unit based on local mean decomposition and Wigner-Ville distribution;Pan;J. Drain. Irrig. Mach. Eng.,2014