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9th Purple Mountain Form on Smart Grid Protection and control, PMF 2024

作者： Wei, Xiaoqiang Liu, Naiyuan He, Zhifeng Yang, Hao Sun, Zhenglong State Grid Heilongjiang Electric Power Company Limited Harbin150000 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132000 China

ISBN: (纸本)9789819616701

There is an urgent need to address the transient synchronization stability (TSS) problem caused by the increasing penetration of renewable resources into the power network through grid-following converters (GFL-VSCs). It is challenging to analyze the TSS in power systems with high penetration of renewable resources due to the complex interaction between a large number of GFL-VSCs and power network. Although some research has been carried out on TSS assessment of GFL-VSC systems, its application is limited because measuring the numerous internal parameters of distributed converters is hard. To fill this gap, this paper proposes a TSS analysis method using local network measurements. The present study first constructs a port energy function for PLL-controlled converters, establishing the relationship between the internal control and the transient energy of the converter. Secondly, the branch energy function is constructed using the law of energy conservation. Due to transient energy interactions between the converters and the branches, the transient instability characteristics of the converters are mapped into the network. Transient instability characteristics are extracted through PMUs in the network. On this basis, the TSS of the GFL-VSC systems is analyzed through the identified critical branches. Finally, the effectiveness of the proposed method is validated through simulations on the modified IEEE 39-BUS system and a practical power system. © state Grid Electric power 2025.

关键词： Transient stability

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Multi-stage image deblurring method based on deep convolutional neural networks

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Journal of Electronic Imaging 2025年第2期34卷

作者： Zhao, Liquan Lin, Yongpei Jia, Yanfei Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Jilin China Beihua University College of Electrical and Information Engineering Jilin China

Due to factors such as relative motion between the imaging device and the target object or out-of-focus optical system, some images may become blurred, severely affecting subsequent image processing tasks. We propose an image deblurring method based on deep convolutional neural networks to recover clear images from blurred ones. The network consists of three structurally similar sub-networks. Each sub-network comprises the multi-scale feature extraction module, single-scale feature extraction module, fast encoding-decoding module, encoding-decoding feature fusion module, and fusion strategy we proposed. The network can extract multi-scale features from input images of different scales, fusing coarse and fine-scale features and ultimately enhancing the clarity of the blurred images. In addition, we introduce the content loss function and frequency reconstruction loss function into the conventional loss function to better measure the quality of deblurred images. Experiments were conducted on the GoPro and HIDE datasets. The HIDE dataset is divided into distant and close-up scenes, allowing for a better evaluation of the generalization ability of deblurring methods in different scenarios. Compared with the existing methods, the experimental results demonstrate that the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index measure values, resulting in clearer and more natural deblurred images. Furthermore, the proposed algorithm exhibits faster image processing speed and smaller model size. The model performs better in terms of real-time capability. © 2025 SPIE and IS&T.

关键词： image deblurring image processing multi-stage neural network

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Analysis of the impact of substation grounding network on the resistive coupling of the surrounding gas pipeline system and the newly built grounding system under high frequency lightning currents

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Dianli Xitong Baohu yu Kongzhi/power system Protection and control 2025年第9期53卷 166-175页

作者： Ma, Chenglian Yao, Xinyue Song, Mengqing Liu, Chunming Yang, Mao Sun, Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China Beijing102206 China

Based on the analysis of the power frequency grounding network, this paper focuses on the upgrading of the old grounding networks in some substations and the safety protection of the surrounding pipeline systems in the power grid, taking a 500 kV substation’s new grounding network renovation project and a 220 kV substation’s grounding network pipeline stray current interference protection project as examples. Based on the four-layer soil model calculated using ETAP power Station and CDEGS, combined with the CDEGS grounding network simulation software, actual models of the new and old grounding networks and surrounding metal pipelines are built. The resistive coupling effects on the grounding networks and its surrounding structures under power frequency and high-frequency currents are compared and analyzed. This paper specifically examines the similarities and differences between the effects of lightning currents and power frequency short-circuit currents on substation grounding networks and surrounding pipeline systems, identifying weak points in the discharge and dispersion process of high-frequency lightning currents. The results show that high-frequency lightning currents cause a sharp increase in the ground potential at the current injection point, with a rapid stepwise decaying of current amplitude, resulting in minimal impact on the ground potential at the edge of the grounding network. When metal pipelines exist around the substation, the sudden rise in transient voltage can easily cause partial discharge in the pipeline insulation layer, leading to a sudden drop in the potential of the gas pipeline insulation layer and resulting in breakdown. © 2025 power system Protection and control Press. All rights reserved.

关键词： Short circuit currents

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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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An Optimal Competitive Bidding and Pricing Strategy for Electric Vehicle Aggregator Considering the Bounded Rationality of Users

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IEEE Transactions on Industry Applications 2025年第3期61卷 4898-4912页

作者： Wang, Qi Huang, Chunyi Wang, Chengmin Xie, Ning Li, Kangping Qiu, Peng Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China Shanghai Jiao Tong University College of Smart Energy Shanghai200240 China State Grid Zhejiang Electric Power Company Electric Power Research Institute Hangzhou310014 China

Accurate estimation of the charging demand of electric vehicle (EV) users has a profound impact on the profitability of the EV aggregator (EVA). Existing studies concerning the market strategy of EVA normally assume the EV users are equipped with complete rationality and simplify the real interactive process among EVAs and EV users, making the derived strategy unable to adapt to the practical situations. To address this bottleneck, a novel competitive bidding and pricing strategy for EVA considering both the bounded rationality of EV users and various game relationships between multiple agents is proposed. Firstly, the evolutionary game theory is utilized to simulate the bounded rational charging behaviors of EV users under differentiated charging prices, while their demand elasticity and uncertainty of driving pattern is also taken into account. Secondly, the complex interactions among EVAs and EV users are modeled, where the competition between EVAs is constructed by a non-cooperative game with incomplete information and the interplay between EVAs and EV users is built as a Stackelberg game. Finally, numerical results validate the effectiveness of the proposed method and demonstrate the pricing strategy preferences of EVAs based on the diverse price elastic coefficients of EV users. © 2025 IEEE. All rights reserved

关键词： Game theory

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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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power grid transformer vibration-noise coupling effect considering subway stray current interference

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Dianli Xitong Baohu yu Kongzhi/power system Protection and control 2025年第9期53卷 70-81页

作者： Pan, Chao An, Jingge Liu, Chuang Li, Zaiyuan Shi, Xiaobo Du, Yi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Ministry of Education Jilin132012 China Dalian Power Supply Company State Grid Dalian Electric Power Co. Ltd. Dalian116001 China

To address the abnormal vibration and noise in power grid transformers caused by stray currents from urban subways, a transformer instability identification method based on the mapping relationship between stray current variations and inherent noise is proposed. First, the primary paths and mechanisms of stray current interference in the power grid are analyzed, and the characteristics of the stray current variations are summarized based on measured data. Next, a multi-physics transformer coupling model considering stray current interference is proposed, dividing the electromagnetic-mechanical-acoustic coupling into an electromagnetic interference domain and a vibration-noise propagation domain. Through simulation analysis under different scenarios, the interference and propagation patterns of stray current mutation rate and timing on transformer parameters such as current and magnetic flux are studied. Finally, an experimental platform is set up to measure the multi-physics characteristics of transformers, while the correctness of the proposed model and conclusions are verified by comparing experimental and simulation results. Moreover, an instability criterion is established based on the mapping between the stray current mutation rate and noise. The results indicate that the vibration and noise of the transformer intensify significantly at points of stray current mutation, with the problem being most severe when the phase angle difference between the stray current mutation timing and the initial phase of power frequency excitation is 90°, leading to an approximately 10% increase in the inherent noise level. This method provides a basis for situational awareness and equipment protection of grid transformers near rail transit systems. © 2025 power system Protection and control Press. All rights reserved.

关键词： Transformer protection

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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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Cepstrum Lifting and Coherent Averaging-Based Instantaneous Frequency Estimation of Low-Amplitude Signal Components for Rotating Machinery

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IEEE Sensors Journal 2025年第10期25卷 17479-17490页

作者： Liu, Shaokang Liu, Shuai Wu, Yingjie Liu, Weiliang Liu, Changliang Luo, Zhihong North China Electric Power University Control and Computer Engineering Department Beijing102200 China Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Northeast Electric Power University Automation Engineering Department Jilin132000 China Inner Mongolia University of Technology Electric Power Department Hohhot010080 China

Instantaneous frequency (IF) serves multiple critical functions in the prognostic and health management of rotating machinery. The current IF estimation methodologies primarily concentrate on enhancing estimation accuracy at the algorithmic level. However, in practical industrial applications, the target signal component is often influenced by varying operating conditions, structural resonances, and background noise, causing it to become submerged or even disappear, ultimately resulting in the failure of conventional IF estimation techniques. To address these challenges, a novel IF estimation approach incorporating signal quality enhancement is developed. First, the complementary multisource vibration signal is processed by the proposed segmented cepstrum lifting (CL) method to overcome the submersion of the resonant frequency band on the target component. Then, the processed segmented signals are fused using coherent averaging (CA) technology to enhance the amplitude of the target component. Finally, leveraging the first-order approximation capability of the short-time fractional Fourier transform (STFrFT) for nonlinear frequency modulation modes, precise IF estimation is achieved. Verified by field data, the proposed method outperforms mainstream methods in both accuracy and applicability, highlighting its industrial application prospects. © 2001-2012 IEEE.

关键词： Frequency estimation

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