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

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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Diagnosis of unbalanced bearing fault samples based on cycle generative adversarial networks

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Neural Computing and Applications 2025年 1-21页

作者： Huang, Rihao Ma, Liangyu Hu, Jingchen Duan, Xiaochong Gao, Haitian Ma, Jin School of Control and Computering Engineering North China Electric Power University Baoding071003 China Baoding Key Laboratory of State Detection and Optimization for Integrated Energy System Baoding071003 China

The robust feature extraction capability of deep learning (DL) has rendered it a popular choice for fault diagnosis in various industries. However, the accuracy of fault diagnosis models is often compromised due to an imbalance between normal and faulty data during the production process. To address this issue, this study proposes an unbalanced bearing fault sample diagnostic method based on a cycle generative adversarial network, using vibration fault diagnosis of rotating machinery rolling bearings as an illustrative example for validation purposes. The method utilizes continuous wavelet transform (CWT) to convert the original vibration signal into time–frequency maps, which are then utilized as input for the cycle generative adversarial network. To address issues related to unstable training and timely convergence of the model, spectral normalization and weight decay techniques are introduced, thereby enhancing the performance of the improved cycle generative adversarial network in generating additional fault samples. Finally, the Swin Transformer model is employed for fault diagnosis and compared with other methods such as Random Forest (RF), Sparse Autoencoder (SAE), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The diagnostic experiment encompasses sample expansion, imbalanced proportion between normal and faulty samples, as well as scenarios involving limited sample sizes. Experimental results demonstrate that the proposed method can generate higher quality synthetic samples when training data is scarce;moreover, utilizing the Swin Transformer for diagnosis yields superior accuracy compared to alternative approaches such as RF, SAE, SVM, or CNN models—thus showcasing significant potential in addressing imbalanced data within fault diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural 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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Master-Slave Game Strategy for Electric Heating Load Group Considering Differences of Heterogeneous Household Types and Aggregation Structure

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 58-66页

作者： Yang, Mao Wang, Jinxin Fang, Guozhong Zhu, Yidan Wang, Yuxin Su, Xin Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Branch of State Grid Corporation of China Shenyang110179 China

The regulation capability of the new power system dominated by renewable energy at the source side is gradually deteriorating. Exploiting the load-side regulation potential to participate in demand response has become an important approach to enhance grid regulation capacity. Firstly, taking distributed residential electric heating clusters as the research object, a dynamic model of temperature-varying power is established for electric heating rooms based on the building thermodynamics theory. Secondly, the impacts of household type differences and aggregated household proportions on the regulation capability of heating load clusters are investigated. A refined aggregation model for heating load clusters considering the differences of heterogeneous household types and aggregation structure is established, with adjustable electricity quantified as an indicator to measure the regulation potential of heating load clusters. Then, a master-slave game model is constructed with the load aggregator (LA) as the leader and cluster users as followers. The quantified adjustable electricity of load clusters is incorporated into the game model as flexible resources to participate in demand response. The upper-level game model establishes an LA pricing model to formulate electricity scheduling plans and optimal heating electricity prices within the system, while the lower-level develops a user payment cost model where users determine their heating schedules based on the electricity prices. Finally, case studies verify that the proposed game model can effectively stimulate the demand response potential of users with the differences of heterogeneous household type, achieving economic benefits for both LA and users. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Cooling

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Analysis of Frequency Dynamic Distribution Characteristics in Chain-structured power system with Renewable Energy Integration

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 49-57页

作者： An, Jun Shi, Yan Cao, Shuai Zhou, Yibo Li, Kexin Zhang, Wanxiang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Branch of State Grid Corporation of China Shenyang110179 China

The integration of renewable energy generation, represented by wind power, into the power system has a significant impact on frequency security, and the characteristics of frequency distribution have gradually become a focal point of attention. To study the influence of wind power integration into multi-machine systems on frequency distribution characteristics, based on the frequency response characteristics of the"grid, source, and load"sides, this paper constructs an analytical model of the frequency response distribution characteristics of a multi-machine system with wind power, and analyzes the impact mechanism of wind power integration on the frequency distribution characteristics. Then a multi-dimensional quantification index system is developed to describe the relative dispersion and absolute differences in frequency response. Next, the validity of the mechanism model is verified using a simple chain-structured power system with"generation-transmission-reception", and the influence rules of electrical distance, wind power integration location, and disturbance location on the frequency distribution characteristic indices are analyzed. Finally, the influence rules of distribution characteristics are analyzed and verified in an actual large-scale power system with a high proportion of renewable energy. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Wind power integration

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Multi-Objective Coordination and Stability control of High Proportion Wind power system Based on K-Domain Tuning Damping 19th

Multi-Objective Coordination and Stability Control of High P...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Qingtao Gao, Diju Zhang, Dan Cai, Haiqing Gu, Haohan Chen, Wei Wu, Wencong Chen, Zhihao Institute of Logistics Engineering Shanghai Maritime University Shanghai China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9789819608966

With the further development of "double high" characteristics of power system, the traditional stability problem dominated by synchronous generator has gradually evolved into a new stability problem dominated by power electronic equipment control. In addition, different types of failure problems can occur in wind power systems. In order to achieve multi-objective coordinated and stable operation of high proportion wind power system, a damping control method with parameters tuning is proposed, which includes the design of filtering link and proportional amplification link. Among them, the key parameters of the proportional link are adjusted. Finally, the feasibility of the proposed control method is verified by simulation tests. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Electric power system stability

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Holomorphic Embedding Searching Method for Voltage Stability Region Boundary of power systems

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 123-134页

作者： Li, Xue Zhi, Menglei Jiang, Tao Li, Guoqing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Static voltage stability region (SVSR) studies power system voltage stability from the perspective of"region", effectively addressing voltage stability issues caused by strong fluctuations and randomness of renewable energy output. To accurately and rapidly construct SVSR, a SVSR boundary (SVSRB) searching method is proposed based on the holomorphic embedding method (HEM). Firstly, a voltage amplitude-active power sensitivity holomorphic embedding function criterion for searching voltage saddle node bifurcation (SNB) points is proposed based on the HEM theory. Secondly, this criterion is combined with a variable step-size control strategy to achieve fast and accurate search of individual SNB point. Then, according to the topological characteristics of SVSRB and utilizing known SNB point information, adjacent SNB points are efficiently solved through fast and flexible holomorphic embedding, thereby realizing efficient multi-dimensional SVSRB search. Finally, cases on test systems such as WECC 9-bus and IEEE 118-bus systems verify that the proposed method enables rapid and accurate construction of SVSRB. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Bifurcation (mathematics)

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Adaptive Adjustment Method for Grid-forming Energy Storage Parameters Based on Buffer Functions and Damping Switching

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Dianli Xitong Zidonghua/Automation of Electric power systems 2025年第10期49卷 77-86页

作者： Cui, Yang Ma, Boyu Dai, Hanyang Ma, Xin Zhao, Yuting Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China China Electric Power Research Institute Beijing100192 China Electric Power Research Institute of State Grid Ningxia Electric Power Ltd. Co. Yinchuan750002 China

In the development tendency of high penetration of renewable energy and power electronic devices in new power systems, the grid-forming control technology for renewable energy integration mitigates the system stability issues caused by insufficient inertia and damping. However, it also reduces the dynamic regulation performance of the system. This paper proposes an adaptive adjustment method for grid-forming energy storage parameters based on buffer functions and steady-state/transient-state damping switching, to improve the dynamic regulation performance of the system. Firstly, the buffer functions are used in inertia adjustment to enhance the regulation smoothness of the system. Secondly, a steady-state/transient-state damping switching link is introduced in the damping adjustment to reduce the steady-state error of active power output during the frequency reduction process. Thirdly, the fuzzy control is employed to introduce correction terms for inertia and damping, and the stability of the proposed method is analyzed. Finally, simulations validate the effectiveness of the proposed method in enhancing small-signal stability and power response dynamic performance of the system. © 2025 Automation of Electric power systems Press. All rights reserved.

关键词： Electric power transmission networks

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Achieving an Accurate Random Process Model for PV power Using Cheap Data:Leveraging the SDE and Public Weather Reports

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CSEE Journal of power and Energy systems 2025年第1期11卷 124-135页

作者： Yiwei Qiu Jin Lin Zhipeng Zhou Ningyi Dai Feng Liu Yonghua Song College of Electrical Engineering Sichuan UniversityChengdu610065China State Key Laboratory of the Control and Simulation of Power Systems and Generation Equipment Tsinghua UniversityBeijing 100087China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao999078China Department of Electrical Engineering Tsinghua UniversityBeijing100087China

Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.

关键词： Photovoltaic(PV)power random process stochastic differential equation(SDE) stochastic optimization time series modeling uncertainty quantification weather report

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Microdynamic modulation through Pt–O–Ni proton and electron“superhighway”for pH-universal hydrogen evolution

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Journal of Energy Chemistry 2025年第2期101卷 808-815,I0018页

作者： Dewen Wang Yuting Chen Bohan Yao Tian Meng Yanchao Xu Dongxu Jiao Zhicai Xing Xiurong Yang State Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022JilinChina University of Science and Technology of China Hefei 230026AnhuiChina Department of Chemistry Shenzhen Grubbs Institute and Guangdong Provincial Key Laboratory of Energy Materials for Electric PowerSouthern University of Science and TechnologyShenzhen 518055GuangdongChina State Key Laboratory of Automotive Simulation and Control School of Materials Science and EngineeringJilin UniversityChangchun 130012jilinChina

Optimizing the microdynamics in alkaline and neutral conditions is a significant but challenging task in developing pH-universal hydrogen evolution(HER)***,a unique Pt-O-Ni bridge has been constructed to alter the coordination and electronic environment between Pt nanoparticles(Pt_n)and nickel metaphosphate(NPO)substrate(Pt-NPO).Sufficient electron transfer from NPO to Pt_n to maintain an electron-rich environment and a low valence state of Pt_***,H*is produced from the H_(2)O dissociation on Ni site and then spillover toward Pt sites to bind into H_(2),which makes up for the insufficient H_(2)O dissociation ability of Pt in Volmer ***-NPO exhibits long-term stability and only need the overpotentials of 22.3,33.0 and 30.5 mV to attain 10 mA cm^(-2)in alkaline,neutral and acidic media,*** anion-exchange membrane(AEM)water electrolyzer catalyzed by Pt-NPO shows high water electrolysis performance that a cell voltage of 1.73 V is needed to obtain the current density of500 mA cm^(-2)in 1 M KOH at 80℃,at the same time maintains good stability for 350 *** regulation strategy proposed in this work is helpful for the design and synthesis of highly efficient pH-universal HER electrocatalysts.

关键词： Hydrogen evolution reactions pH-universal HER electrocatalysts Metal-support interactions Microdynamics Atomic and electronic regulation

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