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
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https://doi.org/10.31449/inf.v49i13.9292Downloads
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