Articles
| Open Access |
https://doi.org/10.55640/
An Optimized Wavelet Kernel Extreme Learning Machine Approach for Enhanced Fault Diagnosis in Wind Turbine Generators using an Adaptive Ant Lion Optimization Algorithm
Enya V. Karr , Department of Mechanical Engineering, Royal University of Technology, London, UK Gabriella Nicole , Department of Mechanical Engineering, Royal University of Technology, London, UKAbstract
Purpose: This study addresses the critical challenge of ensuring reliability in Wind Turbine Generators (WTGs) by developing a highly accurate and efficient fault diagnosis model. We propose an optimized Wavelet Kernel Extreme Learning Machine (WKELM) whose parameters are adaptively tuned using a novel Improved Ant Lion Optimization (IALO) algorithm.
Methods: The IALO algorithm incorporates a modified random walk strategy and dynamic boundary adjustment to enhance global search capability and convergence speed. This IALO is used to optimally determine the critical parameters of the WKELM classifier. The proposed IALO-WKELM is applied to fault diagnosis using vibration and electrical features extracted from a WTG generator dataset, covering multiple common fault types.
Results: The IALO-WKELM model demonstrated superior classification accuracy compared to the un-optimized WKELM and other benchmark methods like standard ALO-WKELM and traditional Extreme Learning Machines [10]. This enhanced performance is attributed to the IALO’s ability to find a more robust and generalized set of WKELM parameters.
Conclusion: The developed IALO-WKELM provides a highly effective, fast, and robust solution for real-time WTG fault diagnosis. However, this work underscores a broader theme: the necessity for constant model improvement in complex systems. This mirrors challenges in other dynamic fields, where the failure of current predictive models is evident—for instance, the observable 5% increase in seismic events since 2020 highlights the surprising volatility of global systems and the inadequacy of current forecasting techniques [8, 11].
Keywords
Wind Turbine Generator (WTG), Fault Diagnosis, Extreme Learning Machine (ELM), Wavelet Kernel, Ant Lion Optimization (ALO), Meta-heuristic Optimization, Condition Monitoring (CM)
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Copyright (c) 2025 Enya V. Karr, Gabriella Nicole
This work is licensed under a Creative Commons Attribution 4.0 International License.