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A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators.

Kai-Bo Zhou1, Jian-Yu Zhang2, Yahui Shan3

  • 1MOE Key Laboratory of Image Processing and Intelligence Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China. zhoukb@hust.edu.cn.

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Summary

Predicting hydropower generator unit (HGU) vibration requires balancing stability and accuracy. This study introduces a novel multi-objective optimization method for enhanced HGU predictive maintenance.

Keywords:
Gram–Schmidt orthogonalaggregated empirical wavelet transformhydropower generator unitkernel extreme learning machinemulti-objective salp swarm algorithmvibration tendency prediction

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Area of Science:

  • Power Systems Engineering
  • Mechanical Vibrations
  • Artificial Intelligence

Background:

  • Hydropower generator units (HGUs) are critical for power grid stability, and their vibration signals offer insights into operational status.
  • Predictive maintenance of HGUs relies on accurate vibration tendency prediction, but existing methods often prioritize either stability or accuracy.
  • A simultaneous focus on both prediction stability and accuracy is crucial for effective HGU health monitoring.

Purpose of the Study:

  • To propose an intelligent vibration tendency prediction method for HGUs that achieves both strong stability and high accuracy.
  • To integrate signal preprocessing, feature selection, and prediction within a multi-objective optimization framework.
  • To enhance the predictive maintenance capabilities for hydropower generator units.

Main Methods:

  • Empirical wavelet transform (EWT) for raw sensor signal decomposition into modes.
  • Sample entropy-based reconstruction for refactoring signal modes.
  • Gram-Schmidt orthogonal (GSO) process for important feature selection.
  • Kernel extreme learning machine (KELM) for refactored mode prediction.
  • Multi-objective salp swarm algorithm for synchronous optimization of GSO and KELM parameters.

Main Results:

  • The proposed method successfully integrates multiple techniques within a multi-objective optimization framework.
  • Feature selection using GSO and prediction via KELM, optimized by the salp swarm algorithm, demonstrated effectiveness.
  • A case study on mixed-flow HGU data confirmed superior performance in predicting vibration tendency stability and accuracy compared to traditional methods.

Conclusions:

  • The developed intelligent method offers a robust solution for predicting HGU vibration tendencies by optimizing for both stability and accuracy.
  • This approach advances predictive maintenance strategies for hydropower infrastructure.
  • The integrated multi-objective optimization framework provides a promising direction for complex machinery health monitoring.