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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Qinfeng Ma Yihua Zhu Mingshun Liu Dongxu Chang Qingxin Pu Chengxiang Li Power Dispatching Control Center of Guizhou Power Grid Co. Ltd Guizhou 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 Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

The stability control system is an important means to ensure the safe and stable operation of the power system. In view of the shortcomings of the existing offline/real-time simulation test methods, a closed-loop simulation scheme based on virtual component technology is proposed. The virtual component technology of the stability control device is researched, and the virtualization method of the external hardware interface of the device is designed. On this basis, a closed-loop simulation framework of the stability control system under the offline/real-time simulation test environment is proposed. The functional logic of each component under the framework are analyzed. The calculation and feedback process of the entire closed-loop simulation system are analyzed in depth, which effectively solves the difficulty of simulation analysis and test verification of large-scale stability control system, providing a new method for the research and verification of stability control system strategy.

关键词： Analytical models Process control system integration power system stability control systems Stability analysis Hardware

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MAE-GAN: A Novel Strategy for Simultaneous Super-resolution Reconstruction and Denoising of Post-stack Seismic Profile

arXiv

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arXiv 2024年

作者： Yu, Wenshuo Dong, Shiqi Lu, Shaoping Dong, Xintong The College of Instrumentation and Electrical Engineering Jilin University Jilin Changchun130026 China The Key Laboratory of Modern Power System Simulation and the Control and Renewable Energy Technology Ministry of Education Jilin Jilin City132012 China Department of Communication Engineering College of Electric Engineering Northeast Electric Power University Jilin Jilin City132012 China School of Earth Sciences and Engineering Sun Yat-Sen University Guangdong Guangzhou510275 China Guangdong Zhuhai519000 China Guangdong Provincial Key Lab of Geodynamics and Geohazards Sun Yat-Sen University Guangdong Guangzhou510275 China

Post-stack seismic profiles are images reflecting containing geological structures which provides a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale attention encoder-decoder network based on generative adversarial network (MAE-GAN) is proposed. This method improves the resolution of post-stack profiles, and effectively suppresses noises and recovers weak signals as well. A multi-scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post-stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post-stack profiles. Experimental results on simulated and field post-stack profiles demonstrate that the proposed MAE-GAN method outperforms two advanced convolutional neural network-based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the MAE-GAN method preserve more geological structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Performance Analysis on Hydrogen-Battery Coordination Storage for renewable energy Accommodation in Large-Scale power system

Performance Analysis on Hydrogen-Battery Coordination Storag...

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Smart power & Internet energy systems (SPIES), International Conference on

作者： Xiaopan Chen Qi Yang Na Meng Yan Cheng Jiang Wu Guanchu Chen MOEKLINNS Xi’an Jiaotong University Xi’an China Xinjiang Key Laboratory of Whole Process Simulation for Power System Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China

energy storage is an effective measure to address the renewable energy curtailment problem in large-scale power systems. However, installing the same type of energy storage has limited improvement to the entire system when the total installed capacity of energy storage is fixed. In this paper, an optimal scheduling method for the hydrogen-battery coordination storage system is proposed to further improve renewable energy accommodation by combining the advantages of different types of energy storage. The coupling relationship between the power grid and the heat grid is also considered a way to reduce energy waste. Case tests based on actual operating data analyze the performance of systems with different energy storage configurations. Results indicate the effectiveness of the proposed method. The optimal installed capacity for hydrogen energy storage and battery energy storage is further researched to provide advice for energy storage planning.

关键词： Couplings renewable energy sources Hydrogen storage Hydrogen Optimal scheduling power grids Planning

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Single-stage Grid-connected Photovoltaic Generation Takes Part in Grid Frequency Regulation for Electromechanical Transient Analysis

Single-stage Grid-connected Photovoltaic Generation Takes Pa...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Yan, Gangui Hu, Wenbo Jia, Qi Zhang, Shanfeng Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781728135205

In order to solve the problem of insufficient frequency regulation capacity caused by the large-scale grid-connected photovoltaic (PV) generation, the active power-frequency droop control strategy and virtual inertia control strategy are proposed for single-stage grid-connected PV generation for electromechanical transient analysis. By modifying the control structure of the inverter, PV generation actively got involved in frequency regulation. The simulation results indicate that: the inverter can control the PV array to increase or decrease the active power generated according to the set droop control and virtual inertia control, so as to suppress the drop or rise of the grid frequency;in the dynamic process of frequency, the incremental power of PV generation induced by the dynamic action of the voltage vector of grid connection point is dependent on its virtual inertia control. © 2019 IEEE.

关键词： Transient analysis

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Super-Resolution Reconstruction of Electric power Inspection Images Based on Very Deep Network Super Resolution 6th

Super-Resolution Reconstruction of Electric Power Inspection...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Shi, Bowen Gao, Jilong He, Zhentao Zhang, Tian Zhong, Tie Chen, Haipeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin CityJilin132012 China College of Electrical Engineering Northeast Electric Power University Jilin CityJilin132012 China School of Physics Northeast Normal University Changchun130024 China

ISBN: (纸本)9783030578831

Aiming at the problem of getting low resolution image and blurred image during unmanned aerial vehicle (UAV) inspection, a super-resolution reconstruction algorithm based on very deep network super resolution (VDSR) is proposed. The algorithm model is composed of deep convolution neural network and residual structure, which is improved on the basis of super resolution convolutional neural network (SRCNN). By deepening the network to 20 layers, the receptive field can be expanded, and the residual structure can be combined to obtain better reconstruction effect. The experimental results show that the proposed super-resolution method based on VDSR has richer texture and more realistic visual effect, with 2.95 dB and 0.037 improvement in PSNR and SSIM compared with the super-resolution methods based on Bicubic Interpolation, Sparse Coding and SRCNN. The proposed algorithm further promotes the theoretical research of inspection image super-resolution, and can be effectively applied to the practical application of power inspection. © 2020, Springer Nature Switzerland AG.

关键词： Textures

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Research on Transforming Effect and Calculation Method of MMC Converter on AC / DC Harmonics

Research on Transforming Effect and Calculation Method of MM...

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2021 IEEE Sustainable power and energy Conference, iSPEC 2021

作者： Cui, Liu Peng, Guangqiang He, Jingsong Wu, Jiyang Mao, Chizu Jiao, Ning Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc GuangZhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System GuangZhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid GuangZhou China Maintenance Test Center Ehv Power Transmission Company Guangzhou China Csg Key Laboratory for Dc Transmission Equipment State Evaluation and Fault Diagnosis GuangZhou China Sichuan University College of Electrical Engineering and Information Technology Chengdu China

ISBN: (纸本)9781665414395

Modular multilevel converter (MMC) is currently the most concerned voltage source converter topology in the field of flexible direct current transmission (VSC-HVDC). Due to its non-linear characteristics, harmonic components will inevitably be generated in AC and DC systems, which may affect the operation of equipment and system, cause protection equipment to malfunction, and cause interference to neighboring communications. The MMC converter is a non-linear device, which can be considered as an amplifying modulator with voltage and current conversion functions. The AC and DC networks are coupled to each other through the MMC converter to generate dynamic harmonic interaction. In recent years, harmonic instability problems have appeared in many VSC-HVDC projects. Compared with other converter topologies, the transfer relationship of harmonics on the AC and DC sides of MMC converters has not been studied in depth. In this paper, by studying the AC/DC harmonic conversion characteristics of MMC converters, it reveals the conversion law of MMC converters between AC/DC harmonics;through theoretical derivation, the quantitative calculation method of MMC harmonic transmission process is given. PSCAD simulation results prove the correctness of the above results. © 2021 IEEE.

关键词： Transmissions

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Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

arXiv

引用

arXiv 2022年

作者： Li, Yang Wei, Xinhao Li, Yuanzheng Dong, Zhaoyang Shahidehpour, Mohammad The Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The Electrical and Computer Engineering Department Illinois Institute of Technology ChicagoIL60616 United States The ECE Department The King Abdulaziz University Saudi Arabia

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified. Copyright © 2022, The Authors. All rights reserved.

关键词： Smart power grids

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Coordination of Electricity and Heat Dispatch Strategy Combining Carbon Trading and Peak Shaving Compensation Mechanism

Coordination of Electricity and Heat Dispatch Strategy Combi...

引用

2020 International Conference on Electronic, Electrical and Computer Applications, ICEECA 2020

作者： Ge, Weichun Li, Zhao Zhao, Dong Li, Zhenyu Ye, Qing Fu, Yu Yu, Na State Grid Liaoning Province Electric Power Company Limited Shenyang110006 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Fuxin Power Supply Company of Liaoning Electric Power Co. Ltd Liaoning Fuxin123000 China

Integrated power generation group's participation in the carbon trading market and peaking auxiliary service market is one of the effective ways to promote low carbon power development and wind power consumption. Aiming at the serious problem of wind abandonment during the low temperature period of winter heating period in Northeast China, a multi-market mechanism is proposed. The carbon trading is analysed. Firstly, and the regional peak demand optimization model based on multi-scenario stochastic programming and the maximum wind power acceptance power calculation model of regional power grid is constructed to calculate the peak shaving demand and existing peak shaving in each period of the regional power grid. Maximum wind power acceptance under resources;Based on the coordination of carbon quotas among power generation groups and the subsection compensation mechanism of compensated peak shaving costs, and aiming at the minimum sum of total coal consumption costs, wind abandonment penalties and compensation costs, integrated dispatching model considering carbon trading and peak shaving compensation is established. Finally, an example is given to verify the rationality and effectiveness of the proposed strategy. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Lithium-Ion Batteries Fault Diagnosis Based on Multi- Dimensional Indicator

Lithium-Ion Batteries Fault Diagnosis Based on Multi- Dimens...

引用

2021 Annual Meeting of CSEE Study Committee of HVDC and power Electronics, HVDC 2021

作者： Wei, Xiao Shuwen, Miao Jun, Jia Qiao, Zhu Yong, Huang Power System and Large-scale Power Generation Equipment Safety Control and Simulation Laboratory Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610031 China Tsinghua Sichuan Energy Internet Research Institute Chengdu610041 China Sichuan Homwee Technology Co.Ltd. Chengdu610041 China

ISBN: (纸本)9781839537035

Since lithium-ion batteries are the core components and main sources of failures in electric vehicles and energy storage systems, fault diagnosis plays a crucial role in the stable operation of lithium-ion batteries. In this paper, a multidimensional indicator-based lithium-ion battery fault diagnosis algorithm is proposed to obtain the weights of different dimensional indicators in the battery fault evaluation system, which applies entropy weight method to calculate the risk coefficients of each individual battery for battery fault diagnosis. The algorithm validation work in this paper is completed in the MIT-Stanford public experimental data set and the actual operation data set of the energy storage system. Firstly, the data feature extraction method suitable for engineering application scenarios is selected. Then, the entropy weight method is used to calculate the weights of each indicator, and then the cause of the battery failure is analysed based on the weight information of each indicator. The method with the entropy of each dimensional indicator to calculate its weight can locate the faulty battery and reduce the subjectivity in the fault analysis process. Meanwhile, the method is suitable for real-time fault diagnosis of lithiumion battery systems without complicated training models and hyper-parameter adjustment processes. © 2021 IET Conference Proceedings. All rights reserved.

关键词： Failure analysis

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power system Transient Stability Assessment Method Based on CapsNet

Power System Transient Stability Assessment Method Based on ...

引用

2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Tian, Xu An, Jun Mu, Gang Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9781728135205

This paper addresses the need to make judgments quickly and accurately after a transient stability failure in the power system. A method for transient stability assessment of power systems based on CapsNet is proposed. This method does not require manual calculation and is based directly on simulation data. Using the self-learning ability of the CapsNet and strong feature extraction ability, the offline training model is used to find the mapping relationship between the input data set and the transient stability, and then online evaluation. This paper effectively improves the accuracy of the evaluation by constructing the input data matrix;By improving the parameters in the network, the calculation speed is effectively improved, and the robustness and generalization ability of the model are enhanced. The idea of "fast measurement, fast resolution, fast control" has been achieved. The simulation results of IEEE-39 node system verify the effectiveness of the proposed method, and the proposed method is superior to the evaluation model of other machine learning methods. © 2019 IEEE.

关键词： Feature extraction

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