Due to advancements in the manufacturing techniques of transformer core materials,the precision of traditional excitation inrush current detection methods has progressively diminished,thereby precipitating the issue of erroneous operation in transformer differential protection systems.To address this issue,a novel CNN-BiLSTM excitation inrush current identification model incorporating an attention mechanism was proposed.Initially,various instantaneous current data were collected using the PSCAD simulation platform,simulating transformer energization,parallel operation,and internal faults,forming the dataset for the model.Subsequently,the CNN-BiLSTM-Attention model was constructed and trained on MATLAB.Finally,comparative experiments are conducted using different models,followed by an analysis of noise resistance.Results demonstrate that the CNN-BiLSTM-Attention neural network not only distinguishes excitation inrush,over-excitation,and fault currents with 100%accuracy,but also exhibits superior differentiation among different fault currents.The model has been shown to exhibit strengths including robust generalization performance,high fitting capacity,and resilient noise resistance.
In modern power systems, the integration of renewable energy sources exacerbates voltage sag issues, which significantly impact power quality. This paper proposes a novel voltage sag measurement method that combines a root mean square (RMS) sliding window difference operator with sampled sequence reconstruction to address the limitations of traditional detection methods. Unlike conventional approaches, the proposed method accurately identifies the start and end times of voltage sag using a predefined threshold, thereby ensuring precise interval isolation for detailed analysis. Moreover, phase jump detection is enhanced through sampled sequence reconstruction. These enable the accurate extraction of voltage sag characteristics, including depth, duration, and phase jump. The simplicity and computational efficiency of the algorithm make it highly suitable for implementation in embedded systems. Comprehensive simulations and real-world measurements have been conducted to validate the effectiveness of the proposed method, demonstrating significant improvements in voltage sag detection accuracy and robustness against noise and other disturbances.
In the reliability analysis of electrical integrated energy systems,traditional numerical algorithms struggle with the high computational demands of natural gas dynamics,making timely dynamic analysis difficult.This paper presents a novel approach that replaces these algorithms with a neural network-based method using multi-scale dilated convolution and attention mechanisms.The model utilizes convolutional neural networks(CNN)for feature extraction and long short-term memory(LSTM)networks to capture time series characteristics.Multi-scale dilated convolutions expand the receptive field,while attention mechanisms enhance sensitivity to critical changes.This sequence-to-sequence learning process accurately models complex relationships between time steps,resulting in a dynamic surrogate model for the gas network.The gas network model is integrated with the power system flow model,allowing for a comprehensive reliability analysis using Monte Carlo methods and multi-state models.Tests on a distribution-level electric-gas integrated energy system show that the CNN-LSTM model not only accurately simulates gas dynamics but also significantly improves computational efficiency,meeting the reliability assessment needs of large-scale integrated energy systems.
This study addresses the limitations of existing indirect bridge damage identification methods that are based on the vehicle-bridge coupled vibration theory of highway bridges. To overcome these shortcomings, we propose an extended approach that incorporates various types of deep-learning models with vehicle-bridge coupled vibration responses. The proposed method is demonstrated using a three-span continuous beam bridge as a case study. First, a vehicle and bridge analysis model is established, and bridge damage is simulated using unit stiffness reduction, considering different damage scenarios. Next, to account for road roughness randomness, vehicle-bridge coupling vibration analysis is performed under various road roughness conditions, yielding the vertical acceleration vibration signal of the vehicle. Subsequently, we employ an end-to-end damage recognition method, utilizing the vehicle acceleration response as the network input, to construct two types of deep-learning models: one-dimensional convolutional neural network (1D-CNN) and convolutional long short-term memory neural network (CNN-LSTM). The recognition performance of both models is compared and analyzed. Taking Zhengzhou Taohuayu Self-Anchored Suspension Bridge in China as an example, this study delves into the capability of bridge damage identification using deep learning. The results demonstrate that the one-dimensional convolutional neural network achieves excellent recognition performance in terms of both damage location and severity.
Current infrared and visible image fusion algorithms often suffer from issues such as unclear texture details in the fused image and an unbalanced display of infrared information and texture details.In this paper,we propose an image fusion method of pseudo-color infrared and visible images based on attention-dense network.The greyscale infrared image is first processed in pseudo-color and then combined with the colored visible image to form a multi-channel data input fusion network.Secondly,a generator network structure consisting of convolutional layers and densely connected blocks with attention modules is designed to focus on the key information of the source image and enhance the ability of the network to extract information from the source image.Finally,the content loss function is constructed by using infrared pixels,visible pixels,visible gradient and infrared gradient to keep the stability of infrared target and texture details in the fused image.Qualitative and quantitative comparisons are made with five representative fusion methods.The results show that the peak signal-to-noise ratio,information entropy,average gradient,and mutual information of the fused images obtained by this method achieve the optimal values of 31.684 1,6.558 1,6.009 6,and 3.096 0,respectively.The quantitative and qualitative results demonstrate that the proposed fusion method results in a fused image with richer texture details and good visual effects.
