Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Luo Xun

doi:10.1117/12.3026553

15 March 2024 Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Luo Xun

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751A (2024) https://doi.org/10.1117/12.3026553
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

With the increasing demand for clean energy in society, the stable operation and efficient management of New Energy Power Generation System (NEPGS) has become a key issue. This research focuses on the real-time monitoring and fault diagnosis of fNEPGS based on Deep Convolutional Neural Network (DCNN), aiming at improving the reliability of the system, reducing downtime and improving maintenance efficiency. First of all, by constructing DCNN model, the real-time monitoring of NEPGS is realized in this study. The model can extract key features and dynamically adapt to the changes of system operation state by learning large-scale monitoring data. The experimental results show that DCNN performs well under normal working conditions and successfully captures the characteristic patterns under different working conditions. Secondly, this study applies DCNN to fault diagnosis, covering a variety of potential fault modes, such as broken teeth, tooth surface wear and so on. By introducing various fault samples into the training set, DCNN can identify and classify different fault types with high accuracy. This provides feasibility for providing real-time and accurate diagnosis when faults occur. The experimental results show that the system in this study has achieved ideal performance on the verification set, all samples have been correctly classified, and the diagnostic accuracy rate has reached 100%. This provides an advanced and feasible solution for health management and real-time monitoring of NEPGS.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Luo Xun "Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751A (15 March 2024); https://doi.org/10.1117/12.3026553

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KEYWORDS

Deep convolutional neural networks

Convolution

Feature extraction

Deep learning

Education and training

Reliability

Diagnostics

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