Mobileception-ResNet for transient stability prediction of novel power systems

Yin, Linfei; Ge, Wei

doi:10.1016/j.energy.2024.133163

Energy

2024

DOI: 10.1016/j.energy.2024.133163

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Mobileception-ResNet for transient stability prediction of novel power systems

Linfei Yin,

Wei Ge

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Cited by 1 publication

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Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning

Zhang,

Tang,

Qin

et al. 2024

Entropy

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Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. To further enhance the efficacy of feature extraction and classification accuracy, a projection head is introduced in this paper to systematically map all sample features into a normalized space, thereby improving the model’s capacity to distinguish between distinct fault types. Finally, through the optimization of a supervised contrastive learning (SCL) strategy, the model can better capture the subtle differences between different fault types. Experimental results show that the proposed method has an accuracy of 99.61%, 97.48%, and 96.22% in the classification task of multiple crack fault types at three speeds, which is significantly better than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), and Transformer methods.

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Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning

Zhang,

Tang,

Qin

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

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Mobileception-ResNet for transient stability prediction of novel power systems

Cited by 1 publication

References 45 publications

Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning

Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning

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