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GD-YOLO:A Network with Gather and Distribution Mechanism for Infrared Image Detection of electrical Equipment

Computers, Materials & Continua 2025年第4期83卷 897-915页

作者： Junpeng Wu Xingfan Jiang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China

As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical *** response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution *** combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel ***,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution ***,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over *** GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,***,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.

关键词： Infrared image detection aggregation and distribution mechanism sample imbalance strategy lightweight structure

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Coordinated control Strategy of New Energy power Generation System with Hybrid Energy Storage Unit

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Energy engineering 2025年第1期122卷 167-184页

作者： Yun Zhang Zifen Han Biao Tian Ning Chen Yi Fan School of Mechano-Electronic Engineering Xidian UniversityXi’an710071China Electric Power Control Center State Grid Gansu Electric Power CompanyLanzhou730030China Electric Power Control Center State Grid Gansu Electric Power Company Zhangye Power Supply BranchZhangye734000China

The new energy power generation is becoming increasingly important in the power *** as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,photovoltaic power generation is unstable and random,resulting in a low utilization rate and directly affecting the stability of the power *** solve this problem,this paper proposes a coordinated control strategy for a newenergy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage ***,the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal,which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage ***,the fuzzy control algorithm is introduced to balance the power between energy *** this paper,the actual data is used for simulation,and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit,effectively avoiding problems such as overshoot and over-discharge,and can significantly improve the stability of the photovoltaic power generation systemand the stability of the Direct Current *** is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.

关键词： Photovoltaic power suppression hybrid energy storage unit variationalmodal decomposition fuzzy control power distribution control

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MPGA-NET: A MULTI-PATH GUIDED ATTENTION NETWORK FOR DESERT SEISMIC NOISE ATTENUATION

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International Journal of Innovative Computing, Information and control 2025年第3期21卷 667-682页

作者： Zhong, Tie Song, Yaling Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education China School of Electrical Engineering Northeast Electric Power University No 169 Changchun Road Jilin 132012 China

Desert seismic exploration is a key technology for detecting natural resources. However, due to the unique surface conditions and complex environmental factors in desert regions, seismic data are often contaminated by strong noise, which poses serious challenges for subsequent data processing. Furthermore, background noise not only exhibits high intensity and a wide frequency band but also overlaps with effective seismic signal in the frequency spectrum, rendering it challenging for traditional signal processing techniques to separate noise from signals. In recent years, denoising models based on convolutional neural network (CNN) have shown significant potential for denoising tasks due to their strong feature extraction and learning capabilities. However, existing models have limitations in feature extraction and representation, which may affect denoising performance. To address this shortcoming, this paper proposes a new Multi-Path Guided Attention Network (MPGA-Net). Specifically, MPGA-Net integrates three paths: the primary core path is responsible for multi-scale feature extraction;the guided enhancement path enhances feature representation;and the attention assisted path focuses on key information. Experimental results show that MPGA-Net not only effectively suppresses noise but also recovers weak reflected signals masked by noise, achieving denoising performance that is significantly superior to both traditional methods and existing CNN approaches. © 2025, ICIC International.

关键词： Convolutional neural networks

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Coordinated Service Restoration of Integrated power and Gas Systems with Renewable Energy Sources

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Energy engineering 2025年第3期122卷 1199-1220页

作者： Xincong Shi Yuze Ji Xinrui Wang Ruimin Tian Chao Zhang College of Electrical Engineering Zhejiang UniversityHangzhou310000China Power Dispatch and Control Center State Grid Shanxi Electric Power CompanyTaiyuan030021China Planning Division of Power Dispatch and Control Center State Grid Jincheng Power Supply CompanyJincheng048000China

With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service *** this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and ***,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic *** objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution *** a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming *** effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node *** comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.

关键词： Service restoration renewable energy sources integrated energy systems extreme events convex optimization

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Abnormal Data Identification and Reconstruction Based on Wind Speed Characteristics

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CSEE Journal of power and Energy Systems 2025年第2期11卷 612-622页

作者： Mao Yang Tian Peng Wei Zhang Xin Su Chao Han Fulin Fan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China Department of Electronic and Electrical Engineering University of StrathclYdeGlasgow G11XWUnited Kingdom

High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed, influence of wind speed fluctuation characteristics on wind power is discussed, and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points, and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data, and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm, results show the method can effectively identify various abnormal data, and complete high-quality reconstruction of data, thereby improving accuracy of wind power prediction.

关键词： Abnormal data bidirectional weighted random forest BiGRU CLOF reconstruction wind power prediction wind speed characteristics

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Graph Attention Network Based Deep Reinforcement Learning for Voltage/var control of Topologically Variable power System

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Journal of Modern power Systems and Clean Energy 2025年第1期13卷 215-227页

作者： Xiaofei Liu Pei Zhang Hua Xie Xuegang Lu Xiangyu Wu Zhao Liu School of Electrical Engineering Beijing Jiaotong UniversityBeijing 100044China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Yunnan Electric Power Dispatching and Control Center Kunming 650011China

The high proportion of renewable energy integration and the dynamic changes in grid topology necessitate the enhancement of voltage/var control(VVC)to manage voltage fluctuations more *** model-based control algorithms are becoming increasingly incompetent for VVC due to their high model dependence and slow online computation *** alleviate these issues,this paper introduces a graph attention network(GAT)based deep reinforcement learning for VVC of topologically variable power ***,combining the physical information of the actual power grid,a physics-informed GAT is proposed and embedded into the proximal policy optimization(PPO)*** GAT-PPO algorithm can capture topological and spatial correlations among the node features to tackle topology *** address the slow training,the Relief F-S algorithm identifies critical state variables,significantly reducing the dimensionality of state ***,the training samples retained in the experience buffer are designed to mitigate the sparse reward ***,the validation on the modified IEEE 39-bus system and an actual power grid demonstrates superior performance of the proposed algorithm compared with state-of-the-art algorithms,including PPO algorithm and twin delayed deep deterministic policy gradient(TD3)*** proposed algorithm exhibits enhanced convergence during training,faster solution speed,and improved VVC performance,even in scenarios involving grid topology changes and increased renewable energy ***,in the adopted cases,the network loss is reduced by 6.9%,10.8%,and 7.7%,respectively,demonstrating favorable economic outcomes.

关键词： Voltage/var control grid topology renewable energy graph attention network deep reinforcement learning

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Superhydrophobic ceramic membrane coupled with a biphasic solvent for efficient CO_(2)capture

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Green Energy & Environment 2025年第4期10卷 834-844页

作者： Kaili Xue Zhen Chen Xiaona Wu Heng Zhang Haiping Chen Junhua Li State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process School of EnergyPower and Mechanical EngineeringNorth China Electric Power UniversityBeijing102206China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing102206China

An innovative strategy was proposed by integration of membrane contactor(MC)with biphasic solvent for efficient CO_(2) capture from flue *** accessible fly ash-based ceramic membrane(CM)underwent hydrophobic modification through silane grafting,followed by fluoroalkylsilane decoration,to prepare the superhydrophobic membrane(CSCM).The CSCM significantly improved resistance to wetting by the biphasic solvent,consisting of amine(DETA)and sulfolane(TMS).Morphological characterizations and chemical analysis revealed the notable enhancements in pore structure and hydrophobic chemical groups for the modified *** of wetting/bubbling behavior based on static wetting theory referred the liquid entry pressure(LEP)of CSCM increased by 20 kPa compared to pristine *** with traditional amine solvents,the biphasic solvent presented the expected phase *** experiments demonstrated that the CO_(2) capture efficiency of the biphasic solvent increased by 7%,and the electrical energy required for desorption decreased by 32%.The 60-h continuous testing and supplemental characterization of used membrane confirmed the excellent adaptability and durability of the *** study provides a potential approach for accessing hydrophobic ceramic membranes and biphasic solvents for industrial CO_(2) capture.

关键词： Carbon capture Membrane contactor Hydrophobic modification Membrane wetting Biphasic solvent

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GENERATIVE ADVERSARIAL NETWORKS FOR REMOTE SENSING IMAGE DEHAZING WITH COLOR FEATURE RESTORATION

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International Journal of Innovative Computing, Information and control 2025年第2期21卷 323-338页

作者： Zhao, Liquan Qin, Yuqing Jia, Yanfei Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University No. 169 Changchun Road Jilin132012 China College of Electric and Information Engineering Beihua University No. 3999 Beijing Road Jilin132013 China

Remote sensing images are often affected by atmospheric factors such as haze during the acquisition process, resulting in blurring and low contrast in the collected remote sensing images. This problem impacts the image quality, thereby affecting the analysis of remote sensing images. To mitigate the impact of haze on remote sensing images, a generative adversarial network is proposed. It comprises a generative network and an adversarial network. Firstly, a novel feature extraction module is designed to enhance the capability of extracting useful information from remote sensing images. It enables the network to focus more on regions with dense haze, allowing it to extract more important information while filtering out irrelevant details. Secondly, a residual attention module is designed which can allocate different weights based on varying haze density in the feature map. This module readjusts features outputted by the encoder, facilitating better image restoration. Thirdly, a multi-scale module is also incorporated to extract feature information across various image scales. Lastly, a color feature extraction module is designed to extract color features. The novel feature extraction module, residual attention module, multi-scale module, and color feature extraction module are utilized for constructing the generative network. Besides, an adversarial network is also designed to indirectly enhance the dehazing capability of the generative network. Synthetic and real datasets are used to test six different methods for dehazing remote sensing images, respectively. The proposed method achieves higher PSNR, SSIM, and lower MSE on the synthetic remote sensing dataset. On the other hand, it achieves lower PIQE, BRISQUE, and higher MetaIQA on the real remote sensing dataset. The proposed method has best performance in dehazing remote sensing images than other methods. © 2025, Int. J. Innov. Comput. Inf. control. All rights reserved.

关键词： Generative adversarial networks

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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Degradation of carbamazepine by the UVA-LED_(365)/ClO_(2)/NaClO process:Kinetics,mechanisms and DBPs yield

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Journal of Environmental Sciences 2025年第2期148卷 399-408页

作者： Chenyan Hu Yihui Wu Zhengyu Dong Ziyi Dong Shengjie Ji Lili Hu Xinyu Yang Hao Liu College of Environmental and Chemical Engineering Shanghai Engineering Research Center of EnergySaving in Heat Exchange SystemsShanghai University of Electric PowerShanghai 200090China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

A mixed oxidant of chlorine dioxide(ClO_(2))and NaClO was often used in water treatment.A novel UVA-LED(_(365) nm)-activated mixed ClO_(2)/NaClO process was proposed for the degradation of micropollutants in this ***(CBZ)was selected as the target *** with the UVA_(365)/ClO_(2) process,the UVA_(365)/ClO_(2)/NaClO process can improve the degradation of CBZ,with the rate constant increasing from 2.11×10^(−4) sec^(−1) to 2.74×10^(−4) sec^(−1).In addition,the consumption of oxidants in the UVA_(365)/ClO_(2)/NaClO process(73.67%)can also be lower than that of UVA_(365)/NaClO(86.42%).When the NaClO ratio increased,both the degradation efficiency of CBZ and the consumption of oxidants can increase in the UVA_(365)/ClO_(2)/NaClO *** solution pH can affect the contribution of NaClO in the total oxidant *** the pH range of 6.0-8.0,the combination process can generate more active species to promote the degradation of *** change of active species with oxidant molar ratio was investigated in the UVA_(365)/ClO_(2)/NaClO *** ClO_(2) acted as the main oxidant,HO·and Cl·were the main active species,while when NaClO was the main oxidant,ClO·played a role in the *** chloride ion(Cl^(-)),bicarbonate ion(HCO_(3)^(-)),and nitrate ion(NO_(3)^(-))can promote the reaction *** the concentration of NaClO in the reaction solution increased,the generation of chlorates will *** UVA_(365)/ClO_(2)/NaClO process can effectively control the formation of volatile disinfection by-products(DBPs),and with the increase of ClO_(2) dosage,the formation of DBPs can also decrease.

关键词： UVA Chlorine dioxide(ClO_(2)) NaClO Carbamazepine(CBZ) Disinfection by-products

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