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Coordinated Suppression Method of Fault Current for DC Grid with Novel Dissipative Resistors Topology

CSEE Journal of power and energy systems 2024年第6期10卷 2371-2383页

作者： Shouqi Jiang Huanyu Zhao Guoqing Li Yechun Xin Lixin Wang Weiru Wang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China.

A DC grid based on half-bridge modular multilevel converters(HB-MMC)is a feasible means to realize the friendly grid connection of renewable *** solve problems such as the high cost and technical difficulty of DC circuit breakers(DCCB),a coordinated control method for fault current suppression of DC grid connecting wind power is *** key influencing factors of DC fault current are revealed by fault characteristics analysis,and an adaptive current-limiting control method for MMC is proposed,whose parameter selection principles are designed to ensure the safe operation of equipment while achieving effective suppression of fault *** addition,a novel configuration method of dissipative resistors with the current-limiting function is proposed,which can solve the problem of surplus power in the DC grid and reduce the current stress of converter *** on this,a coordination scheme of dissipative resistors,the adaptive current-limiting control method,and DCCBs are proposed to block fault current,effectively reducing the manufacturing difficulty and cost of ***,a four-terminal DC grid simulation model is built based on the RTLAB OP5600 real-time digital simulation platform,and the effectiveness and feasibility of the proposed methods are verified.

关键词： Adaptive current-limiting control coordinated suppression method dissipative resistor DC grid parameter selection principle

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Extraction and application of intrinsic predictable component in day-ahead power prediction for wind farm cluster

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energy 2025年 328卷

作者： Mao Yang Renxian Jiang Xinnan Yu Bo Wang Xin Su Chenglian Ma The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing 100912 China

With the continuous updating and iteration of artificial intelligence algorithms, prediction models emerge one after another, but the research and utilization of the predictability of wind power is still less. Therefore, this paper proposed a day-ahead power prediction method for Wind Farm Cluster (WFC) based on intrinsic predictable component extraction. Firstly, based on the difference of power distribution between wind farm and WFC, a method of Wind power Curve (WPC) modeling for WFC is proposed, which provides the basis for establishing the Final Set of Wind power Curve (FSWPC). Secondly, the Intrinsic Predictable Component (IPC) of wind power is extracted based on the FSWPC, and the Interference Component (IC) of wind power is separated to eliminate the influence of IC on IPC in the process of prediction. Thirdly, the historical similarity matching method with large threshold is used to predict the IC to make up for the numerical deficiency of the IPC. Finally, the proposed method was applied to a WFC in China to verify its effectiveness. Compared with the traditional prediction strategy, the NRMSE, NMAE and MAPE are reduced by 1.59 %, 1.15 % and 6.42 %, respectively.

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FDCA-DSTGCN: A Wind Farm Cluster power Day-ahead Prediction Model Based on Frequency Domain Information Gain and Dynamic Trend Sensing

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IEEE Transactions on Sustainable energy 2025年

作者： Yang, Mao Niu, Jiajun Wang, Bo Huang, Dawei Su, Xin Ma, Chenglian Ministry of Education Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing100912 China

Accurate wind farm cluster power prediction (WFCPP) is of vital significance for new power systems with large-scale wind power integration. The current WFCPP modeling method ignores the important role of wind direction and frequency domain information, resulting in insufficient use of spatial information and difficult to improve the prediction accuracy. In order to solve the above problems, a day-ahead power prediction model for wind farm cluster power is proposed based on information gain in frequency domain and dynamic trend sensing. Firstly, a cluster division method based on graph theory and progressive fusion of multiple information is proposed, and a virtual information node is set up. Secondly, a method to identify prevailing wind direction under time window is proposed for subsets, and a dynamic weighted directed graph structure is designed based on prevailing wind direction and wind speed. Thirdly, a dynamic spatio-temporal graph convolutional network model with frequency domain gain channel attention mechanism (FDCA-DSTGCN) is proposed to integrate the above information and complete the prediction. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia of China. Compared with the existing and traditional prediction methods, the normalized root mean square error of the proposed method is reduced by 1.13% -2.15%, which verifies the effectiveness of the proposed method. © 2010-2012 IEEE.

关键词： Mean square error

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Fault Location Detection of Transmission Lines in Noise Environments Based on Random Matrix Theory

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CSEE Journal of power and energy systems 2022年第4期8卷 1233-1241页

作者： Jun An Zihan Deng Haipeng Chen Gang Mu Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012.Jilin ProvinceChina

Fault detection and location are critically significant applications of a supervisory control system in a smart *** methods,based on random matrix theory(RMT),have been practiced using measurements to detect short circuit faults occurring on transmission ***,the diagnostic accuracy is infuenced by the noise signal in the *** relationship between mean eigenvalue of a random matrix and noise is detected in this paper,and the defects of the Mean Spectral Radius(MSR),as an indicator to detect faults,are theoretically determined,along with a novel indicator of the shifting degree of maximum eigenvalue and its *** comparing the indicator and the threshold,the occurrence of a fault can be ***,an augmented matrix is constructed to locate the fault *** proposed method can effectively achieve fault detection via the RMT without any influence of noise,and also does not depend on system *** experiment results are based on the IEEE 39-bus ***,actual provincial grid data is applied to validate the effectiveness of the proposed method.

关键词： Fault detection maximum eigenvalue noise random matrix theory smart grid

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Differential Feature Fusion and Gated Coordinate Attention Mechanism for Few-Shot Object Detection of Electrical Equipment Images

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Journal of Engineering 2025年第1期2025卷

作者： Wu, Junpeng Zhou, Zhi Xian, Ran Tao, XinGuang Liu, Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China School of Electrical Engineering Northeast Electric Power University Jilin China

The over-dependence on the amount of labelled images in the process of deep learning-based object detection of electrical equipment images leads to the consumption of manpower and material resources. To solve this problem, this paper proposes a few-shot detection method of electrical equipment based on a two-stage fine-tuning approach and the improved YOLOv7 framework. First, an improved gated coordinate information attention (GCA) is introduced into the backbone part to reduce the negative impact of irrelevant background information on feature extraction. Second, in the neck part, an adaptive spatial feature fusion with difference information (ASFFDI) network is designed to improve the multi-scale feature extraction ability. Finally, the optimised α-SIoU loss function is used to improve the performance of the model. In addition, the two-stage fine-tuning training approach (TFA) is applied to the improved YOLOv7 model. The experimental results show that the average accuracy of the proposed method reaches 33.0%, 45.7% and 65.2%, respectively in 3 way-10 shot, 3 way-20 shot and 3 way-30 shot, which can realise few-shot detection of electrical equipment images. © 2025 The Author(s). The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Electric appliances

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Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical power system Considering Cross-Space Cascading Failure

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energy Engineering 2024年第9期121卷 2525-2542页

作者： He Wang Xingyu Tong Huanan Yu XiaoHu Jing Bian The Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132000China

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power ***,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these *** this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication *** this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)*** with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus *** results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

关键词： Cyber-physical power system service function chain risk balance routing optimization cascading failure

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Excitation Current and Vibration Characteristics of DC Biased Transformer

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CSEE Journal of power and energy systems 2021年第3期7卷 604-613页

作者： Chao Pan Chengjian Wang Hao Su Key Laboratory of Modem Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China

In practical engineering,DC disturbance will lead to excitation saturation and vibration intensification on transformer core,as well as affecting the normal operation of equipment and power *** to the problem,a new idea is proposed in this *** on the electromagnetic characteristics of DC biased transformer,excitation saturation and core vibration are studied by using excitation-vibration harmonic response analysis *** current is identified through electromagnetic coupling to characterize the excitation saturation ***,vibration harmonic response model is established to analyze core *** characteristics of DC biased Y/△transformer are *** variation of excitation current and core vibration at different DC levels are ***-vibration characteristics of three-phase group transformer and three-phase three-limb transformer are compared and *** experimental platform built,the correctness of simulation results is verified by experimental data,and the effectiveness of the proposed method can be proved.

关键词： DC disturbance excitation-vibration harmonic response Y/△transformer

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Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

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energy Engineering 2024年第6期121卷 1535-1555页

作者： Huanan Yu Hangyu Li He Wang Shiqiang Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization ***, a measurement point distribution optimization model is formulated, leveraging compressive *** model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

关键词： Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network

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Integrated energy microgrid bi-level game scheduling optimization taking into account electricity-heat-hydrogen enriched compressed natural gas coupling and shared energy storage

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International Journal of Electrical power & energy systems 2025年 169卷

作者： Changgang Wang Xiaoning Sun Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China

Hydrogen-enriched compressed natural gas (HCNG) is a promising technology capable of significantly reducing carbon emissions in conventional gas units. Its integration into microgrids enhances the coupling and complementarity of diverse energy sources. Shared energy storage systems, which play a crucial role in improving energy utilization, have garnered increasing attention in recent research. However, the deployment of HCNG technology involves multiple stakeholders, posing challenges in balancing their respective benefits. To address this, we propose a novel scheduling method for Integrated energy Management that accounts for electricity-heat-HCNG coupling and shared energy storage services. First, we model the proton exchange membrane electrolyzer and HCNG energy coupling device, incorporating HCNG loads on the demand side to enhance hydrogen production and utilization efficiency. Additionally, tiered carbon trading mechanisms, integrated demand response strategies, and shared energy storage are introduced to optimize carbon reduction on both the demand and storage sides while effectively constraining energy emissions. The strategic interactions between integrated energy microgrid operators and load aggregators are modeled using a Stackelberg game approach, and the existence and uniqueness of equilibrium solutions are rigorously proven. Finally, the proposed model is solved using the Black-winged Kite algorithm in conjunction with the CPLEX *** studies demonstrate the effectiveness of the proposed method, showing revenue increases of 9.15% for integrated energy microgrid operators and 8.29% for load aggregators, alongside a reduction in carbon emissions by 8.76%.

关键词： Integrated energy microgrid HCNG Stackelberg game Carbon trading Demand respond

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A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

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Journal of modern power systems and Clean energy 2020年第6期8卷 1060-1069页

作者： Jun An Jiachen Yu Zonghan Li Yibo Zhou Gang Mu the Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University) Ministry of EducationJilin 132012China China Electric Power Research Institute Beijing 100192China

Transient stability assessment(TSA)based on security region is of great significance to the security of power *** this paper,we propose a novel methodology for the assessment of online transient stability *** with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency *** network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar *** proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.

关键词： Security region computer-vision-based power flow image(CVPFI) transient stability margin convolutional neural network(CNN) comprehensive network

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