Fault Diagnosis Method for Hydropower Units Based on Dynamic Mode Decomposition and the Hiking Optimization Algorithm–Extreme Learning Machine

Dan Lin (), Yan Wang, Hua Xin, Xiaoyan Li, Shaofei Xu, Wei Zhou and Hui Li
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Dan Lin: Department of Electrical Engineering, Xi’an Electric Power College, Xi’an 710032, China
Yan Wang: Department of Electrical Engineering, Xi’an Electric Power College, Xi’an 710032, China
Hua Xin: Department of Electrical Engineering, Xi’an Electric Power College, Xi’an 710032, China
Xiaoyan Li: Department of Electrical Engineering, Xi’an Electric Power College, Xi’an 710032, China
Shaofei Xu: Department of Electrical Engineering, Xi’an Electric Power College, Xi’an 710032, China
Wei Zhou: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Hui Li: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2024, vol. 17, issue 20, 1-21

Abstract: The diagnosis of vibration faults in hydropower units is essential for ensuring the safe and stable operation of these systems. This paper proposes a fault diagnosis method for hydropower units that combines Dynamic Mode Decomposition (DMD) with an optimized Extreme Learning Machine (ELM) utilizing the Hiking Optimization Algorithm (HOA). To address the issue of noise interference in the vibration signals of hydropower units, this study employs DMD technology alongside a thresholding technique for noise reduction, demonstrating its effectiveness through comparative trials. Furthermore, to facilitate a thorough analysis of the operational status of hydropower units, this paper extracts multidimensional features from denoised signals. To improve the efficiency of model training, Principal Component Analysis (PCA) is applied to streamline the data. Given that the weights and biases of the ELM are generated randomly, which may impact the model’s stability and generalization capabilities, the HOA is introduced for optimization. The HOA-ELM model achieved a classification accuracy of 95.83%. A comparative analysis with alternative models substantiates the superior performance of the HOA-ELM model in the fault diagnosis of hydropower units.

Keywords: dynamic mode decomposition; extreme learning machine; hiking optimization algorithm; hydropower unit; principal component analysis; vibration fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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