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

作者： Jingxiang Zhang Hui Zou Hewei Hou Junhui Li Rongrong Ji Kaiyuan Duan Cuiping Li Xingxu Zhu Extra-High Voltage Branch Company of State Grid Zhejiang Electric Power Co. Ltd Hangzhou China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

At present, substations usually use electrical blocking facilities to achieve the function of anti-fault blocking, but new stations and new reconstruction and expansion stations have the problem of low efficiency in the compilation and review of anti-fault blocking logic rules, and also need a lot of manual debugging and checking time. In this paper, based on the typical substation wiring and anti-false blocking logic rules, the typical substation wiring library and the corresponding blocking logic library are constructed; then, the graphic editing module and graphic analysis module are constructed by combining the substation modelling method with the topology and other technologies. Finally, the automatic generation of anti-error latching logic rules is realised, which solves the problems of low efficiency and lack of reliability in the writing and checking of the corresponding substation anti-misoperation locking logic rules.

关键词： Wiring Graphics Substations Reviews Debugging system integration Libraries Topology Logic Reliability

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Economic Analysis and Visual simulation Platform Construction of Distributed energy Storage on Load Peak-shaving and Valley-filling in Distribution Network 4

Economic Analysis and Visual Simulation Platform Constructio...

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4th IEEE China International Electrical and energy Conference, CIEEC 2021

作者： Sun, Zhebin Saren, Gaowa Liang, Tao Li, Hengzhen Liu, Pengxuan Sun, Dapeng Yan, Jiaqi Ma, Dexuan Zhang, Hao Li, Cuiping Li, Junhui Inner Mongolia Power Economic Technology Research Institute Hohhot China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9781728171494

This paper proposes an economic analysis method for distributed energy storage applications in distribution networks, and constructs a visual simulation platform. Firstly, the influence of photovoltaic / wind power fluctuation on load peak-valley difference in distribution network under different permeability is analyzed, and the technical and economic characteristics of different distributed energy storage systems are compared. Secondly, in order to facilitate the analysis and display the analysis results, a visual simulation platform is constructed based on MATLAB / Simulink software. The simulation results of IEEE 33-Bus system show that the method described in this paper can effectively analyze the economy of load peak-shaving and valley-filling by different energy storage configuration methods, and the visualization platform can clearly and intuitively show the results of the above analysis method. © 2021 IEEE.

关键词： MATLAB

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Zoning Voltage Coordination control Method for High Proportion Distributed Photovoltaic power in Distribution Network

Zoning Voltage Coordination Control Method for High Proporti...

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energy Engineering and power systems (EEPS), International Conference on

作者： Bing Xue Yanfeng Ge Chuang Li Niankang Guo Chenglian Ma Dawei Huang State Grid Yingkou Power Supply Company State Grid Liaoning Electric Power Co. LTD Yingkou Liaoning China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

The high penetration of distributed photovoltaic generation into distribution networks has become a major development trend in the future. However, the intermittency and random fluctuations of distributed photovoltaic output have exacerbated voltage violation and fluctuation issues in distribution networks. To address this problem, a distribution network zoning voltage coordination control strategy is proposed. In terms of zoning, voltage sensitivity index information obtained from daily power flow calculations is utilized to analyse and express the impact variation of photovoltaic generation on the voltage of each node. Based on the threshold requirements of voltage variation indices, the zoning of nodes is determined. In terms of zoning voltage coordination control, a strategy of voltage sensitivity control within the partition is adopted, pairing nodes within each partition and utilizing the active and reactive regulation capabilities of photovoltaic generation to control all over-limit voltage within normal range. Furthermore, through the interval distributed voltage coordination control strategy, the interval state variables are equivalenced, and based on the SADMM algorithm, the dynamic reactive power compensation amount between adjacent partitions is optimized to achieve the optimization and suppression of network-wide voltage fluctuations. Taking the IEEE 33-node system as an example, a case study was conducted to verify the effectiveness and feasibility of the proposed strategy in practical applications.

关键词： Photovoltaic systems Reactive power Fluctuations Voltage fluctuations Sensitivity Distribution networks Regulation Indexes Voltage control Optimization

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Critical link identification of power system vulnerability based on modified graph attention network

arXiv

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arXiv 2024年

作者： Wang, Changgang Wang, Xianwei Cao, Yu Li, Yang Lü, Qi Zhang, Yaoxin Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

As the power grid expands and the proportion of renewable energy increases, the uncertainty and randomness of the power grid have increased, posing a threat to the security of system operation. It becomes crucial to identify the crucial links in the power grid to ensure its reliability during operation. Addressing the issues of slow identification speed and difficulty in meeting the actual operational requirements of traditional methods for identifying crucial links in the power grid, this paper proposes a method based on the Modified Graph Attention Network (MGAT) algorithm for identifying crucial link in the power grid. Firstly, combining complex network theory with actual power grid operational data, an evaluation index set is established. Subsequently, the MGAT is utilized to explore the mapping relationships between various indicators during power grid operation and the crucial links, forming a model for crucial link identification. Considering the requirements of training accuracy and efficiency, the original data is preprocessed to optimize the model. Next, through simulation, the original dataset is obtained for training, validation, and testing of the identification model. Finally, the proposed model is applied to an improved IEEE 30-node system and a real power grid. The results indicate the feasibility of the proposed method, with accuracy and speed surpassing traditional methods, demonstrating its practical engineering value. © 2024, CC BY-NC-ND.

关键词： Smart power grids

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Ultra-Short-Term Wind power Interval Prediction Based on Fluctuating Process Partitioning and Quantile Regression Forest

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FRONTIERS IN energy RESEARCH 2022年 10卷

作者： Sun, Yong Huang, Yutong Yang, Mao Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Jilin Electric Power Company Ltd. Changchun China

As errors in point forecasts of wind power are unavoidable, interval forecasts can adequately describe the uncertainty in wind power and thus provide further guidance to dispatchers in their decision making. Current interval prediction methods are still incomplete in terms of tapping into the physical variability of wind power, especially for the specific time scale of the ultra-short term. This paper therefore proposes a new framework for interval forecasting of ultra-short-term wind power that incorporates the power fluctuation process. Firstly, a fluctuating process of wind power series is defined and a Kalman-SOM method for clustering the fluctuating processes of wind power is constructed. Secondly, a quantile regression forest interval prediction model is constructed for multiple fluctuation processes for ultra-short-term time scales. Finally, the effectiveness of the overall framework is validated at a wind farm in Jilin Province, China. Compared with the traditional interval prediction method. The interval bandwidth is reduced by 0.86% on average, and the interval coverage is increased by 1.4% on average. The results demonstrate the effectiveness and feasibility of the method in this paper.

关键词： wind power ultra-short-term prediction self-organizing map fluctuation quantile regression forest

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Optimal Scheduling Strategy for Interconnected Multi-region Peaking Resources Considering Network Currents

Optimal Scheduling Strategy for Interconnected Multi-region ...

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power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Junhui Li Yahui Pan Xuesong Qi Xiaozhuo Guan Tiebin Guo Yizhu Zhang Jia Sun Yixin Lu Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Jilin China Power dispatching and control center Jilin Power Supply Company Jilin China Science and Technology Section Jilin Power Supply Company Jilin China Science and Technology Innovation Department Jilin Electric Power Co Changchun China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

Under the background of the increasing proportion of new energy power generation, the mismatch of “source-load” occurs between regions. To effectively improve the utilization rate of peaking resources and increase the amount of “source-load” matching, this paper proposes the optimal scheduling of multiregional peaking resources considering network currents. The strategy takes into account the impact of wind power penetration rate on the demand for peaking resources and connects the regions with different peaking characteristics using contact lines. It evaluates and iterates the peaking resources of each region in each time period and finds the optimal amount of peaking resources and the optimal output of thermal power units in each region. Through simulation, the results show that the proposed strategy effectively improves wind power consumption and system operation economy.

关键词： Electrical engineering systems operation Asia Optimal scheduling Wind power generation

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An Optimal Scheduling Method for Shared energy Storage Based on Wind power Uncertainty

An Optimal Scheduling Method for Shared Energy Storage Based...

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power Science and technology (ICPST), IEEE International Conference on

作者： Baoju Li Zezhong Liu Jiyue Fu Junhui Li Xiaobiao Fu Xingxu Zhu Yao Wang Cuiping Li Power Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Ji Lin China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education(Northeast Electric Power University) Ji Lin Ji Lin China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

In order to improve the utilization of energy storage, to explore the value of energy storage utilization, and to study the utilization of energy storage. In this paper, a shared energy storage optimization scheduling method based on wind power uncertainty is proposed. Firstly, the scenario generation method of Latin hypercubic sampling and Cholesky decomposition is used to quantify the wind power output, and the uncertainty output is converted into deterministic output; then the economic operation is taken as the optimal scheduling objective, and environmental benefits such as carbon capture and grid constraints are considered to carry out the optimized scheduling of shared energy storage; finally, the validity of the method is verified through the scheduling of the 6-node arithmetic example system, and the utilization of the energy storage is analyzed. Finally, the effectiveness of the method is verified by scheduling in a 6-node arithmetic system, and energy storage utilization is analyzed.

关键词： Uncertainty Thermal decomposition Optimal scheduling Wind power generation Hypercubes Thermal analysis Scenario generation

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Partial Discharge Fault Diagnosis in power Transformer Based on Symplectic Geometry Mode Decomposition and Optimized Convolutional Neural Networks

SSRN

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SSRN 2023年

作者： Shang, Haikun Liu, Zhidong Huang, Tao Zhang, Ranzhe Li, Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Electric Power Research Institute Xinjiang830011 China

To address challenges in feature extraction, such as difficulty in accurately extracting valuable information and low recognition accuracy during pattern recognition, this paper proposes a novel method for power transformer PD fault diagnosis based on symplectic geometry mode decomposition (SGMD) and optimized Convolutional Neural Networks (CNN). First, three different types of power transformer PD fault signals are extracted, namely floating discharge (FD), corona discharge (CD), and bubble discharge (BD), in a laboratory environment. Second, PD signals are decomposed using SGMD to obtain different symplectic geometric components (SGCs). Afterwards, approximate entropy (ApEn) values of each SGC are calculated as PD feature parameters. Subsequently, the sparrow search algorithm (SSA) algorithm is utilized to optimize and configure the CNN parameters to enhance the diagnostic accuracy and stability. Finally, the obtained feature parameters are fed into the optimized CNN for fault diagnosis. simulation and experimental analysis verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

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Long-Foresight-Term power Prediction Considering the Evolution of Significant Weather Information for Wind Farm Cluster

SSRN

引用

SSRN 2023年

作者： Yang, Mao Jiang, Renxian Zhang, Xiaobin Huang, Yutong Guo, Yunfeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Gansu Electric Power Company Gansu730030 China

The current wind power prediction horizon extends up to 7 days, and the research on power prediction for the 8 to 15 day-ahead is still lacking. However, with the increasing proportion of renewable energy in novel power systems, breaking through the long-foresight-term power prediction barrier is crucial support for guaranteeing the secure and stable operation of the power system. To address the aforementioned issues, this study presents a power prediction method for wind farm cluster (WFC) in long-foresight-term, based on the poor availability of existing numerical weather prediction (NWP) in long-foresight-term. Firstly, a significant wind process identification method considering autocorrelation analysis is employed to predict future power trends, and an optimal method is explored for power generation (PG) prediction in the 8 to 15 day-ahead. Secondly, under the constraint of predicted PG, the power corresponding to the historical similar wind process is selected as the predicted value. Finally, the proposed methodology is applied to a WFC in Gansu Province, China. The results indicate that the proposed method reduces the average RMSE and MAE by 4.15% and 3.3%, respectively, effectively tracking the changing trends of wind power. Moreover, it achieves long-foresight-term prediction and enhances the reliability of the prediction. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

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arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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