Fault Diagnosis and Prediction of New Energy Equipment Based on Large Models
Abstract
With the increasing demand for stability in new energy equipment operations, this paper proposes a dynamic feature extraction and fault prediction algorithm (A-GAN-FP) that integrates the attention mechanism and the generative adversarial network (GAN) for efficient fault diagnosis and prediction. Leveraging the attention mechanism, the algorithm adaptively captures key temporal-spatial features in high-dimensional, non-stationary operation data of new energy equipment. The GAN module enhances feature variability and representativeness through adversarial training, addressing data complexity and class imbalance. Experiments on real wind farm data (covering 100,000 samples across normal/gearbox/generator fault conditions) demonstrate that A-GAN-FP achieves 96.5% fault diagnosis accuracy (15.2%/12.8% improvements over SVM/BP neural networks) and 20–30% RMSE reduction in fault prediction, with an average warning time extension of 2.5 hours.References
Liu, H., Song, X., & Zhang, F. (2021). Fault diagnosis of new energy vehicles based on improved machine learning. Soft Computing, 25(18), 12091–12106. https://doi.org/10.1007/s00500-021-05860-9
Xia, M., Shao, H., Ma, X., & De Silva, C. W. (2021). A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Transactions on Industrial Informatics, 17(10), 7050–7059. https://doi.org/10.1109/TII.2021.3056867
Filcek, G., & Miroforidis, J. (2024). A General Framework for Providing Interval Representations of Pareto Optimal Outcomes for Large-Scale Bi- and Tri-Criteria MIP Problems. Informatica, 35(2), 255-282. https://doi.org/10.15388/24-INFOR549
Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023). Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet of Things Journal, 10(13), 11093–11116. https://doi.org/10.1109/JIOT.2023.3239944
Fanjiang, Y., Lee, C., Du, Y., & Horng, S. (2021). Palm Vein Recognition Based on Convolutional Neural Network. Informatica, 32(4), 687-708. https://doi.org/10.15388/21-INFOR462
Zhang, W., Hao, H., & Zhang, Y. (2024). State of charge prediction of lithium-ion batteries for electric aircraft with Swin transformer. IEEE/CAA Journal of Automatica Sinica, 12(3), 645–647. https://doi.org/10.1109/JAS.2023.124020
Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence, 52(12), 14246–14280. https://doi.org/10.1007/s10489-022-03344-3
Lang, W., Hu, Y., Gong, C., Zhang, X., Xu, H., & Deng, J. (2021). Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review. IEEE Transactions on Transportation Electrification, 8(1), 384–406. https://doi.org/10.1109/TTE.2021.3110318
Tao, H., Qiu, J., Chen, Y., Stojanovic, V., & Cheng, L. (2023). Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion. Journal of the Franklin Institute, 360(2), 1454–1477. https://doi.org/10.1016/j.jfranklin.2022.11.004
Schweidtmann, A. M., Esche, E., Fischer, A., Kloft, M., Repke, J. U., Sager, S., & Mitsos, A. (2021). Machine learning in chemical engineering: A perspective. Chemie Ingenieur Technik, 93(12), 2029–2039. https://doi.org/10.1002/cite.202100083
Huang, K., Wu, S., Li, F., Yang, C., & Gui, W. (2021). Fault diagnosis of hydraulic systems based on deep learning model with multi-rate data samples. IEEE Transactions on Neural Networks and Learning Systems, 33(11), 6789–6801. DOI: 10.1109/TNNLS.2021.3083401
Sridharan, N. V., & Sugumaran, V. (2025). Convolutional neural network based automatic detection of visible faults in a photovoltaic module. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 47(1), 6270–6284. https://doi.org/10.1080/15567036.2021.1905753
Huang, T., Zhang, Q., Tang, X., Zhao, S., & Lu, X. (2022). A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems. Artificial Intelligence Review, 55(2), 1289–1315. https://doi.org/10.1007/s10462-021-09993-z
Sun, L., & You, F. (2021). Machine learning and data-driven techniques for the control of smart power generation systems: An uncertainty handling perspective. Engineering, 7(9), 1239–1247. https://doi.org/10.1016/j.eng.2021.04.020
Omitaomu, O. A., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548–568. https://doi.org/10.3390/smartcities4020029
Mojumder, M. R. H., Hasanuzzaman, M., & Cuce, E. (2022). Prospects and challenges of renewable energy-based microgrid system in Bangladesh: A comprehensive review. Clean Technologies and Environmental Policy, 24(7), 1987–2009. https://doi.org/10.1007/s10098-022-02301-5
DOI:
https://doi.org/10.31449/inf.v49i13.9292Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
