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Optimal Configuration Planning of Multi-energy Microgrid Based on Source-load-temperature Scenarios Deep Joint Generation

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CSEE Journal of power and energy Systems 2023年第3期9卷 1090-1102页

作者： Nantian Huang Wenting Wang Guowei Cai School of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University)Jilin 132012China

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme ***,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are ***,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather *** the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are ***,the scenarios are reduced by clustering *** normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by ***,the optimal configuration of a multi-energy microgrid system is carried *** show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the *** proposed method can accurately capture the complementary potential of energy *** the economy of the system configuration is improved by 14.56%.

关键词： Deep joint generation denoising variational autoencoders multi-energy microgrid system optimal configuration planning

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

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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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The Day-ahead Scenario Generation Method for New energy Based on an Improved Conditional Generative Diffusion Model

arXiv

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arXiv 2025年

作者： Wang, Changgang Liu, Wei Cao, Yu Liang, Dong Li, Yang Mo, Jingshan Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China School of Electrical Engineering Northeast Electric Power University Jilin Province Jilin132012 China Jilin Power Supply Company Substation Secondary Maintenance Center Jilin Province Jilin132012 China

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability. © 2025, CC BY-NC-ND.

关键词： Markov chains

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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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A method for generating wind power output scenarios based on improved conditional generative diffusion model

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Electric power Systems Research 2025年 247卷

作者： Liu, Wei Wang, Changgang Cao, Yu Liang, Dong Li, Yang Mo, Jingshan Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Jilin132012 China School of Electrical Engineering Northeast Electric Power University Jilin Jilin132012 China Jilin Power Supply Company Substation Secondary Maintenance Center Jilin Province Jilin132012 China

With the large-scale integration of wind power into the grid, accurately characterizing wind power uncertainty is crucial for power system *** address this, we propose an improved conditional generative diffusion model(ICGDM) for generating wind power output *** model is built upon a theoretical framework combining Markov chains and variational inference. First, a forward process transforms historical data into pure noise. Then, a reverse process, guided by conditional information, reconstructs scenarios that conform to the target distribution. Additionally, the noise schedule is modified to a cosine function, enhancing the quality of generated ***, the method is applied to wind power output data from the Flanders region. Results demonstrate that the proposed approach effectively and reliably generates wind power output scenarios with strong adaptability. © 2025 Elsevier B.V.

关键词： Markov chains

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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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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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Fuzzy optimal scheduling of hydrogen-integrated energy systems with uncertainties of renewable generation considering hydrogen equipment under multiple conditions

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Applied energy 2025年 393卷

作者： Song, Jianzhao Wang, Na Zhang, Zhong Wu, Hao Ding, Yi Pan, Qingze Pan, Xingzuo Shui, Siyuan Chen, Haipeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gansu Branch of Luneng New Energy Group Co. Ltd. Lanzhou730000 China Lanzhou Power Supply Company State Grid Gansu Electric Power Co. Ltd. Lanzhou730000 China TBEA Shenyang Transformer Group Co. Ltd. Shenyang110144 China Northeast Electric Power University Jilin132012 China

Developing hydrogen-integrated energy systems (HIES) represents a cutting-edge strategy for harnessing renewable energy (RE). However, the inherent unpredictability and variability of RE significantly increase the operational uncertainty of HIES, which leads to severe energy curtailment issues when HIES integrates large-scale RE, necessitating greater operational flexibility in the system. To address the challenges, this paper proposes a fuzzy optimal scheduling approach for HIES that considers the hydrogen equipment under multiple operating conditions. The analysis begins by examining the operational mechanisms of hydrogen equipment. Subsequently, a multi-conditions model is developed for electrolyzers, while a reserve model is established for hydrogen fuel cells. Furthermore, the fuzzy chance constraint (FCC) is employed to quantify the uncertainty of RE generation. An integrated demand response mechanism is implemented, incorporating human thermal comfort and building thermal inertia. Finally, a fuzzy optimization scheduling model for HIES is constructed to minimize the total operating costs. The crisp equivalent of FCC is derived to solve this model, thereby transforming the scheduling model based on fuzzy chance-constrained programming into a solvable mixed-integer programming model. The simulation results indicate that the proposed scheduling method can reduce the overall costs of the HIES by 17.98 % and increase the RE accommodation rate by 19.67 %, validating the effectiveness of the method in enhancing the operational flexibility of the HIES. In addition, this study achieves a balance between economic efficiency and reliability, offering a better economy, lower energy curtailment rates, and faster decision-making times compared to robust optimization, scenario analysis, and other common fuzzy optimization methods. © 2025

关键词： Risk perception

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Improved Strategy of Grid-Forming Virtual Synchronous Generator Based on Transient Damping

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energy Engineering 2024年第11期121卷 3181-3197页

作者： Lei Zhang Rongliang Shi Junhui Li Yannan Yu Yu Zhang Key Laboratory of Advanced Manufacturing and Automation Technology Education Department of Guangxi Zhuang Autonomous RegionGuilin University of TechnologyGuilin541006China Key Laboratory of Modern Power SystemSimulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132011China

The grid-forming virtual synchronous generator(GFVSG)not only employs a first-order low-pass filter for virtual inertia control but also introduces grid-connected active power(GCAP)dynamic oscillation issues,akin to those observed in traditional synchronous *** response to this,an improved strategy for lead-lag filter based GFVSG(LLF-GFVSG)is presented in this ***,the grid-connected circuit structure and control principle of typical GFVSG are described,and a closed-loop small-signal model for GCAP in GFVSG is *** causes of GCAP dynamic oscillation of GFVSG under the disturbances of active power command as well as grid frequency are *** this basis,the LLF-GFVSG improvement strategy and its parameter design method are ***,the efficiency of the proposed control strategy in damping GCAP dynamic oscillations under various disturbances is verified using MATLAB simulations and experimental comparison results.

关键词： Grid-forming virtual synchronous generator first-order low-pass filter lead-lag filter small-signal model parameter design

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Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

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Computers, Materials & Continua 2024年第6期79卷 4395-4411页

作者： Chunming Wu Shupeng Zheng Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Ministry of Education Northeast Electric Power UniversityJilin132012China

Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success *** address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios,a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network(MSCNN)and Long Short-Term Memory(LSTM)fused with attention mechanism is *** adaptively extract the essential spatial feature information of various sizes,the model creates a multi-scale feature extraction module using the convolutional neural network(CNN)learning *** learning capacity of LSTM for time information sequence is then used to extract the vibration signal’s temporal feature *** parallel large and small convolutional kernels teach the system spatial local *** gathers temporal global features to thoroughly and painstakingly mine the vibration signal’s characteristics,thus enhancing model ***,bearing fault diagnosis is accomplished by using the SoftMax *** experiment outcomes demonstrate that the model can derive fault properties entirely from the initial vibration *** can retain good diagnostic accuracy under variable load and noise interference and has strong generalization compared to other fault diagnosis models.

关键词： Bearing fault diagnosis convolutional neural network short-long-term memory network feature fusion

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