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Vibration Trend Prediction of Hydropower Unit Based on VMD-SE-PSO-BIGRU
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Bo FU1, Xing-yu NIE1, Xi-lin ZHAO1, Shi-wen WANG1, Chao-shun LI2
Water Resources and Power | 2023, 41(12) : 178 - 181
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Water Resources and Power | 2023, 41(12): 178-181
• ELECTROMECHANICS AND CONTROL ENGINEERING •
Vibration Trend Prediction of Hydropower Unit Based on VMD-SE-PSO-BIGRU
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Bo FU1, Xing-yu NIE1, Xi-lin ZHAO1, Shi-wen WANG1, Chao-shun LI2
Affiliations
  • 1.College of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
  • 2.College of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20222338
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In order to improve the prediction accuracy of vibration signals of hydropower units, a prediction method based on variational mode decomposition (VMD), sample entropy (SE) reconstruction and particle swarm optimization (PSO) optimization of bi-directional gated circulation unit (BIGRU) was proposed. In the VMD-SE-PSO-BIGRU model, firstly, the vibration signal was decomposed into several subsequences by VMD, and the subsequences were reconstructed by SE. The trend, oscillation and noise components of the vibration signal were obtained. Then, parameter optimized BIGRU prediction models were established for the reconstructed components. Finally, the prediction results of each component were superimposed to achieve vibration prediction. Compared with other models, example analysis shows that the prediction error of the model is smaller and the prediction accuracy is higher, which can effectively predict the vibration signals of hydropower units.

vibration trend prediction  /  variational modal decomposition  /  sample entropy  /  particle swarm optimization  /  bidirectional gated circulation unit
Bo FU, Xing-yu NIE, Xi-lin ZHAO, Shi-wen WANG, Chao-shun LI. Vibration Trend Prediction of Hydropower Unit Based on VMD-SE-PSO-BIGRU[J]. Water Resources and Power, 2023 , 41 (12) : 178 -181 . DOI: 10.20040/j.cnki.1000-7709.2023.20222338
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Year 2023 volume 41 Issue 12
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222338
  • Receive Date：2022-11-07
  • Online Date：2026-01-28
  • Published：2023-12-25
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  • Received：2022-11-07
  • Revised：2023-03-27
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Affiliations
    1.College of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    2.College of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222338
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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