Design and analysis of wind turbine fault diagnosis system based on convolutional neural network
1
Scientific Research Department, Hunan Electrical College of Technology, Xiangtan, 411101, China
2
Institute of Big Data and Artificial Intelligence Application Technology, Hunan Electrical College of Technology, Xiangtan, 411101, China
3
School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan City, 411201, China
Submission date: 2024-06-28
Final revision date: 2024-11-29
Acceptance date: 2025-02-11
Online publication date: 2025-02-18
Publication date: 2025-02-18
Corresponding author
Xiaoli Luo
Scientific Research Department, Hunan Electrical College of Technology, Xiangtan, 411101, China
Scientific Research Department, Hunan Electrical College of Technology, Xiangtan, 411101, China
KEYWORDS
TOPICS
ABSTRACT
Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation.
FUNDING
The research is supported by achievement of the 2024 Hunan Provincial Natural Science Foundation project "Intelligent Fault Diagnosis and Remaining Life Prediction of Wind Turbine Units Based on Deep Learning" (No. 2024JJ7095), as well as the 2023 Hunan Provincial Natural Science Foundation project "Key Technologies Research of Multi-source Data Fusion and Application for Intelligent Control of Wind Farms Aimed at Enhanced Absorption" (No. 2023JJ60183).
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| eISSN: | 2449-5220 |
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