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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river

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Journal of Environmental Sciences 2025年第2期148卷 375-386页

作者： Xiaxia Li Kai Chen Chao Wang Tianyu Zhuo Hongtao Li Yong Wu Xiaohui Lei Ming Li Bin Chen Beibei Chai School of Water Conservancy and Hydroelectric Power Hebei University of EngineeringHandan 056038China Collaborative Innovation Center for Intelligent Regulation and Comprehensive Management of Water Resources School of Water Conservancy and HydroelectricHebei University of EngineeringHandan 056038China Hebei Key Laboratory of Intelligent Water Conservancy Hebei University of EngineeringHandan 056038China Tianjin Water Group Binhai Water Co. Ltd.Tianjin 300308China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China School of Architecture and Civil Engineering Xi’an University of Science and TechnologyXi’an 710054China Chengdu University of Technology Chengdu 610059China College of Natural Resources and Environment Northwest A&F UniversityYangling 712100China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijing 100875China

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River—which is the longest river in *** phytoplankton are sensitive indicators of trophic changes inwater bodies,characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution ***,we used direct microscopic count and environmental DNA(eDNA)metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin(Chengdu,Sichuan Province,China).The association between phytoplankton community structure and water environmental factors was evaluated by Mantel *** environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation *** the phylum level,the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta,Chlorophyta,and Cyanophyta,in contrast with Chlorophyta,Dinophyceae,and Bacillariophyta identified by eDNA ***α-diversity analysis,eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic *** growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios>16:1 in all water *** analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton *** results could be useful for implementing comprehensive management of the river basin *** is recommended to control the discharge of point-and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients(e.g.,Jianyang-Ziyang).Algae monitoring techniques and removal strategies should be improved in 201 Hospital,Hongrihe Bridge and Colmar Town areas.

关键词： Environmental DNA Microscopic count Phytoplankton growth Structural equation modeling Tuojiang River Basin

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation

arXiv

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arXiv 2025年

作者： Dong, Xiaochong Liu, Yilin Zhang, Xuemin Mei, Shengwei State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China

Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution modeling, formulating regression tasks for both marginal distributions and the correlation structure using proper scoring rules to ensure the rationality of the modeling process. The scenario generation process is divided into two stages. Firstly, the dynamic correlation network models temporal correlations based on a dynamic covariance matrix, capturing the time-varying features of renewable energy while enhancing the interpretability of the black-box model. Secondly, the implicit quantile network models the marginal quantile function in a nonparametric, continuous manner, enabling scenario generation through marginal inverse sampling. Experimental results demonstrate that the proposed dynamic correlation quantile network outperforms state-of-the-art methods in quantifying uncertainty and capturing dynamic correlation for short-term renewable energy scenario generation. The code is open-source and available at: https://***/DREAMDXC/Dynamic-Correlation-Quantile-Network Copyright © 2025, The Authors. All rights reserved.

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Monitoring Conductor Sags for Dynamic Line Rating Based on Induced Signals in Overhead Ground Wires

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IEEE Transactions on power Delivery 2025年

作者： Cui, Zherui Zhang, Bo Wang, Xiankang Yao, Yao Zhou, Xueming Tsinghua University Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Beijing100084 China State Grid Hubei Electric Power Research Institue Wuhan430000 China

The sag information of conductors is important for dynamic line rating. This paper proposes a novel monitoring method for conductor sags based on the current or voltage in ground wires, which is produced by mutual inductance between conductors and ground wires. The devices of this method are easy to install and maintain, and the monitoring effect is not affected by bad weather such as snow and fog. To obtain sag information, a calculation method of induced signals in the ground wires is firstly proposed. Then the sensitivity of the induced signals in the ground wires to the variation of conductor sags is evaluated. The relationship between the induced signals in the ground wires and the sag information is established through neural network. A scaled test model of overhead transmission line is established to verify the proposed method. The accuracy of deduced results is acceptable. © 1986-2012 IEEE.

关键词： Wire

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Probabilistic resilience assessment of urban distribution power grids by fast inference of multi-source multi-terminal network reliability

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Reliability Engineering and system Safety 2025年 261卷

作者： Yan, Yunqi Chen, Ying Cui, Zhengda Xiao, Tannan Department of Electrical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

Urban power distribution grids featuring loopy topologies and integrated distributed generations pose significant challenges for efficient and precise resilience quantification against disruptive events. This paper presents a probabilistic resilience assessment framework tailored for such grids. Risk metrics grounded in loss of load probability (LOLP) and expected energy not served (EENS) are formulated to evaluate resilience across multiple temporal stages. A multi-source multi-terminal network reliability (MSMT-NR) modeling approach is proposed to characterize the stochastic impact of component failures on load point connectivity. A computationally efficient algorithm framework is developed for the inference of the MSMT-NR problem, comprising: (1) Derivation of analytical LOLP expressions for grid topologies exhibiting tree-like load subgraphs;(2) A deletion–contraction decomposition technique generating solvable tree subgraphs from arbitrary network structures;(3) A computational graph-based inference methodology enabling efficient MSMT-NR evaluation and automatic differentiation for sensitivity analysis of component importance measures. Strategies for enhancing scalability to large-scale grids are devised. Extensive case studies on a real-world 30,894-node distribution grid corroborate the efficiency and precision of the proposed approach. © 2025 Elsevier Ltd

关键词： power distribution reliability

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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A Short-Term power Prediction Method for Wind Farm Cluster Based on Time Dynamic Graph Network and Improved Transformer Model

A Short-Term Power Prediction Method for Wind Farm Cluster B...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Chao Han Zihang Chen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Jiangmen Power Supply Bureau of Guangdong Power Grid Co. Ltd Jiangmen China

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

In recent years, wind power has gradually replaced thermal power as the main source of new energy generation. Wind power prediction in advance can help formulate scheduling strategies and improve the stability of the power system. With the advent of the Industry 4.0 and the widespread application of artificial intelligence technology, artificial algorithms have been extensively used in the field of wind power prediction. To further explore the spatiotemporal correlation of every wind farm in the wind farm cluster, a temporal dynamic graph network is used to divide the wind farm farm into several sub-clusters. Then, the KAN-BiLSTM-Transformer algorithm is used to predict wind power of each wind sub-cluster. Finally, the wind power of each sub-cluster is added to attain the final predicted power of the wind farm cluster. After verification, the RMSE and MAE of the proposed method is 0.06849 and 0.05805 respectively, with an accuracy of 93.15%, which is superior to the compared algorithms. Proposed method can provide more accurate prediction results.

关键词： Accuracy Heuristic algorithms Clustering algorithms Wind power generation Wind farms Predictive models Prediction algorithms Transformers Spatiotemporal phenomena Wind forecasting

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Prediction of Distributed Photovoltaic Accommodation Capability Based on Ridge Regression 9th

Prediction of Distributed Photovoltaic Accommodation Capabil...

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

作者： Ji, Xingquan Zhang, Bowen Zhang, Yumin Sun, Pengkai Zhang, Xiaofeng Liu, Wei dong Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China Information and Communication Company State Grid Shandong Electric Power Company Jinan250013 China Qingdao Yihe Electric Group Co. Ltd. Qingdao266510 China

ISBN: (纸本)9789819616701

A prediction method of distributed photovoltaic accommodation capability is proposed based on ridge regression. First, the factors influencing the distributed photovoltaic accommodation capacity are analyzed, and the concept of the contribution degree of distributed photovoltaic accommodation is introduced. The gray relation analysis method is used to evaluate the correlation between different factors and distributed photovoltaic accommodation, and a model of factors affecting distributed photovoltaic accommodation is constructed. Then, a prediction model of distributed photovoltaic accommodation capability is established based on ridge regression. The factors with high photovoltaic accommodation contribution degrees are incorporated in the prediction model. The mapping relationship between the driving factors of distributed photovoltaic accommodation and the prediction model is derived, then the development strategies for future photovoltaic accommodation capacity are formulated. Finally, simulation is performed on the SPSSPRO platform, and the results show that the proposed method can predict the distributed photovoltaic accommodation capability accurately and provide strategic suggestions for improving future photovoltaic accommodation capacity. © state Grid Electric power 2025.

关键词： Photovoltaics

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Investigation of mechanical characteristics of decay-like degraded FRP rods and residual life prediction

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Engineering Failure Analysis 2025年 176卷

作者： Liu, Shuqi Li, Te Zuo, Zhou Tao, Ruixiang Lei, Ting Wang, Qian Wu, Chao Liang, Xidong State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing China State Grid Zhejiang Electric Power Co Electric Power Research Institute Huadian road Xiacheng District Zhejiang Hangzhou China

Decay-like fracture accidents have posed significant threats to the safe operation of the power transmission lines, and the time interval between the intermediate stage of decay-like degradation and final fracture remains challenging to predict. The present work investigated the residual mechanical strengths of several 500 kV composite insulators that exhibited decay-like degradation, and their degradation extent was quantified by analyzing the cross-sectional degradation areas of the fiber reinforced polymer (FRP) core rods using parallel light illumination method. A clear negative correlation was observed between the cross-sectional decay-like degradation areas and the residual mechanical strengths. Moreover, visual examination and microscopic observation of the fracture spots were conducted, and the fracture modes of different samples were classified as either decay-like fractures or splitting fractures. The macroscopic and microscopic morphologies of the fracture spot of individual sample reflected its unique fracture cause (pure mechanical fracture or stress corrosion fracture), which is directly related to the mechanical strength exhibited by the sample. Additionally, acoustic emission (AE) technique was employed during the tensile fracture test, and the signals were categorized via clustering algorithm. Finally, the correlation between the farthest heating location and the residual mechanical strength was analyzed. It was proposed that a farthest heating location exceeding the 4th large shed can significantly increase the risk of fracture for the in-service decay-like degraded composite insulators. © 2025

关键词： Fracture toughness

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