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• The vulnerable branches which affect the transient voltage security are identified. • Image-form samples combine grid structure and power flow information visually. • Knowledge visualization makes CNN-based transient voltage stability assessment model more understandable and reliable. • A comprehensive vulnerability indicator integrates the knowledge in CNN, grid topology and branch state. • The proposed method is demonstrated on a real-life provincial grid with renewable energy integration. Because of the increasing proportion of grid-connected converters, the transient voltage problem has increased the risk of the secure and stable operation of power systems. As the extension of transient voltage stability assessment (TVSA), transient voltage vulnerability assessment is the key technique to detect the crucial elements which can cause the insecurity risk of power system transient voltage. Although deep learning-based TVSA models have demonstrated the high accuracy, it is more important to display the knowledge learnt by the models and utilize it to recognize the weakness of transient voltage. This paper presents a transient voltage vulnerability assessment method by knowledge visualization of convolutional neural network (CNN). First, the image-form sample construction method is proposed to characterize the power system operating condition, which prepares for training CNN-based TVSA model. Then, the knowledge in TVSA model is visualized based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Activation Maximization (AM) algorithms, which is analyzed from the perspective of power system stability. Finally, a comprehensive vulnerability indicator is proposed for identify the weakness of transient voltage, which combines the knowledge inside the TVSA model and the indicators with clear physical meaning. Simulation results of the New England 39-bus test system and the real Shandong provincial power grid in China demonstrate that the visualization makes the model more reliable and the proposed method can accurately identify the vulnerable branches. [ABSTRACT FROM AUTHOR]

• A long-term water quality prediction model with higher accuracy. • A graph convolutional network obtains topological information among sequences. • A subsequence cross-fusion network captures local features of a sequence. • A destationarizing attention weakens the distribution shift problem of sequences. Water quality prediction is important for maintaining water stability and control in marine environments. However, water quality parameters are affected by complex environmental factors.Marine time series data exhibit distribution shift and nonstationarity problems, owing to environmental heterogeneity.It is still difficult to obtain the spatial and time dependence of time series data with existing models and the model prediction accuracy cannot be guaranteed. Therefore, this paper proposes a graph-based convolutional neural network model, allowing networks and sequences to interact smoothly. The Time-graph convolutional fusion network(T-GCFN) consists of a graph convolutional fusion network(GCFN) and Time-pyramidal fusion attention(T-PFA). To obtain the spatial dependence of the original sequence, the original sequence is first decomposed into three subsequences by STL, and then the GCFN obtains the original sequence and trinomial subsequence through a multilayer T-GCN.Internal topology information is also obtained. Second, to eliminate the distribution differences between the training and test sets, the T-PFA is weakened using RevIN.To address the distribution shift problem of the sequence and the nonstationarity problem of the destationarized attention attenuation sequence, a higher weight is assigned to the sequence. Finally, depending on the long and short time series of the obtained sequence, the subsequence is downsampled by cross-learning, and the local features are extracted. Experiments on dissolved oxygen, salinity and temperature at six marine ranches on the Shandong peninsula were carried out using the T-GCFN, and the results were compared with those of other deep learning models. The experimental results show that the T-GCFN has better prediction performance and can achieve high-precision predictions of ocean chemistry parameters in the next three days. [ABSTRACT FROM AUTHOR]

• Built an annotated bridge damage dataset, which contained bridge damage images. • A novel model replaces the common convolutional function with DeformableConv2D. • The BR-DETR allows the sampling grid to be freely deformed. • Convolutional Project Attention was also added after self-attention layer. • Locally-enhanced Feed-Forward (LeFF) was used to replace the feedforward. Bridges play an important role in modern transportation systems and road networks, and hence it is essential to use various models based on visual inspection to detect and prevent the damages on the surface of bridge structure. However, due to the limitation of traditional models or lack of modelling data, bridge damages are often difficult to be accurately detected. This paper proposed a novel deep learning model called Bridge Detection Transformers (BR-DETR) based on Detection Transformers (DETR). Through analysis of existing bridge damage instances, we used a copy-paste data augmentation method to create new samples and significantly increased the sample size. Convolution was replaced by Deformable Conv2D, which introduces two-dimensional offsets to the regular grid sampling positions of standard convolution. Convolutional Project Attention was also added after the self-attention layer, which enabled additional modeling of local spatial context. In each encoder and decoder layer, Locally-enhanced Feed-Forward (LeFF) was used to replace the Feedforward to promote the correlation between adjacent tokens in the spatial dimension. The BR-DETR model outperformed the DETR model in detection performance with increased mAP and recall on the augmented highway bridge damage dataset and on the augmented Shandong bridge damage dataset. [ABSTRACT FROM AUTHOR]

• Built an annotated bridge damage dataset, which contained bridge damage images. • A novel model replaces the common convolutional function with DeformableConv2D. • The BR-DETR allows the sampling grid to be freely deformed. • Convolutional Project Attention was also added after self-attention layer. • Locally-enhanced Feed-Forward (LeFF) was used to replace the feedforward. Bridges play an important role in modern transportation systems and road networks, and hence it is essential to use various models based on visual inspection to detect and prevent the damages on the surface of bridge structure. However, due to the limitation of traditional models or lack of modelling data, bridge damages are often difficult to be accurately detected. This paper proposed a novel deep learning model called Bridge Detection Transformers (BR-DETR) based on Detection Transformers (DETR). Through analysis of existing bridge damage instances, we used a copy-paste data augmentation method to create new samples and significantly increased the sample size. Convolution was replaced by Deformable Conv2D, which introduces two-dimensional offsets to the regular grid sampling positions of standard convolution. Convolutional Project Attention was also added after the self-attention layer, which enabled additional modeling of local spatial context. In each encoder and decoder layer, Locally-enhanced Feed-Forward (LeFF) was used to replace the Feedforward to promote the correlation between adjacent tokens in the spatial dimension. The BR-DETR model outperformed the DETR model in detection performance with increased mAP and recall on the augmented highway bridge damage dataset and on the augmented Shandong bridge damage dataset. [ABSTRACT FROM AUTHOR]