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3rd International Conference on Smart Energy and Electrical Engineering, SEEE 2024

作者： Hao, Ling Xu, Hui Tan, Zhenlong Zheng, Zhibao Xu, Fei Yang, Ruixuan Xu, Zhenyu Dai, Xuhui State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Co. Ltd. Beijing101199 China School of Economics and Management Tsinghua University Beijing100084 China State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing400044 China

The increase in the proportion of renewable energy generation capacity has placed significant pressure on thermal power operations. Constructing energy storage facilities within the metering output of thermal power plants, allowing them to participate in peak load regulation with generating units jointly, is one effective way to alleviate the operational pressure on thermal power. To achieve comprehensive scheduling of thermal power plants with energy storage, this paper first establishes an overall output model for the coupling operation of thermal power and energy storage. Secondly, a staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage is proposed to solve the optimization scheduling problem for the industrial park involving thermal power plants with energy storage participation. Finally, the simulation results based on a specific regional power grid in Inner Mongolia validate the feasibility of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Thermoelectric power plants

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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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Heterogeneous ensemble short-term integrated load forecasting considering differentiated cycle-trend properties

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Huang, Nantian Wang, Yaoyao Li, Bingling Cai, Guowei Zhang, Liang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology (Northeast Electric Power University) Ministry of Education Jilin Province Jilin 132012 China

Aiming at the Integrated Energy system (IES), there are key problems such as the potential change pattern of seasonal differentiation of multiple loads, the lack of mining complex coupling characteristics, the limited generalization ability of forecasting models and the weak interpretability. A heterogeneous integrated short-term multiple load forecasting method considering differentiated cycle-trend characteristics is proposed. Firstly, Multiple Seasonal Trend decomposition using Loess (MSTL) is used to decompose the multivariate loads into multi-timescale cycle and trend series, and to construct multi-timescale cycle-trend feature sets, which breaks through the limitations of the traditional single-cycle decomposition in characterizing inter-seasonal coupling characteristics. Secondly, the evolutionary multi-objective optimization mechanism based on the dual indexes of diversity and accuracy is introduced into the Stacking integration framework for the first time, and the seasonal adaptive optimal model combination selection of electric/cooling/heating loads is realized. Finally, a two-layer interpretable analysis paradigm based on Shapley Additive Explanations (SHAP) values is established to improve the interpretability of the proposed multivariate load forecasting method by performing input feature attribution analysis from both global and individual dimensions. The experimental analysis is carried out on the example of Tempe Campus IES of Arizona state University in the U.S.A. The results show that the average percentage errors of electricity, cooling, and heating forecasts of the proposed method reach 1.783 %, 1.012 %, and 2.225 %, respectively, demonstrating superior forecasting accuracy compared to existing methods. © 2025 Elsevier Ltd

关键词： Evolutionary multi-objective optimization algorithm Heterogeneous ensemble Interpretability Multivariate load forecasting Stacking ensemble learning

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Quantitative detection of alkali metal Na based on laser-induced breakdown spectroscopy technology

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Meitan Xuebao/Journal of the China Coal Society 2025年第4期50卷 2262-2270页

作者： Shen, Sijie Li, Chi Zhou, Wangzheng Wang, Zhenzhen Yan, Junjie Hou, Zongyu Wang, Zhe Deguchi, Yoshihiro School of Energy and Power Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China Department of Energy and Power Engineering Tsinghua University Beijing100084 China Shanxi Research Institute for Clean Energy Tsinghua University Taiyuan030032 China Graduate School of Technology Industrial and Social Sciences Tokushima University Tokushima770-8506 Japan

The alkali metal compounds released from high alkali fuels such as low-quality coal during thermal utilization can affect the normal operation of thermal equipment. Therefore, rapid online detection of alkali metal content in low-quality coal is of great significance for controlling alkali metal release during combustion. The alkali metal Na element is used as the detection object, and the mixed samples of graphite and sodium chloride powder with different ratios are used as experimental samples. The influencing factors of measuring Na element in samples using laser induced breakdown spectroscopy (LIBS) technology are studied. The impact of two signal strength calculation methods on signal stability is compared. The influence of experimental parameters on signal strength and signal-to-noise ratio is analyzed. The quantitative calculation models for Na element have been established. Research has shown that the characteristic spectral lines of Na element, Na I 588.995 nm and Na I 589.592 nm, are suitable as the main analytical spectral lines. Using the area intensity of the dual line characteristic spectral lines of Na element as the signal intensity can effectively improve signal stability. When the laser energy is 60 mJ and the delay time is 1 000 ns, the relative standard deviation of spectral signal intensity is low and the signal-to-noise ratio is high. The quantitative calculation models are established using traditional calibration method, partial least squares (PLS) method, and support vector machine (SVR) with spectral signal intensity as the input and Na element addition in the sample as the output. The accuracy of each model is compared and analyzed. The results indicate that the PLS model may exhibit overfitting when the sample size is small and the input quantity is large. The fitting accuracy of the SVR model is 0.978 3, the root mean square percentage error of the training set is 13.42%, and the root mean square percentage error of the test set is 13.51

关键词： Sodium chloride

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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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Distributionally robust electricity-carbon collaborative scheduling of integrated energy systems based on refined joint model of heating networks and buildings

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Renewable Energy 2025年 251卷

作者： Chen, Haipeng Song, Jianzhao Li, Zhiwei Shui, Siyuan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China Department of Electrical Engineering Northeast Electric Power University Jilin 132012 China School of Automation and Engineering Northeast Electric Power University Jilin 132012 China Northeast Electric Power University Jilin 132012 China

The rapid increase in the grid integration demand of intermittent renewable energy (RE) has raised higher requirements for the flexible regulation and energy storage capacity of integrated energy systems (IES) with combined heat and power (CHP) units. To solve the issues of efficient accommodation of high proportion RE and low-carbon economic operation of the system, this study proposes an optimal scheduling method for IES based on a refined joint model of heating networks and buildings. Firstly, a refined thermal energy transmission model integrating heating networks and buildings is established to accurately depict the dynamic thermal energy transmission process within the system. Then, this paper proposes an electricity - carbon demand response considering the dynamic carbon emission factor, guiding users to use electricity in a low-carbon way. A distributionally robust optimization (DRO) scheduling model is established to quantify the impact of RE output uncertainty, which is efficiently solved using the column and constraint generation algorithm. The simulation results show that the proposed DRO model ensures the robustness of system operation while improving low-carbon economic benefits. Notably, the RE consumption rate is increased by 9.52 % compared with traditional scheduling methods, which provides an essential reference for promoting the sustainable development of IES. © 2025

关键词： Thermal energy

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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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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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Application of photoelastic effect for excitation signal detection and optimization in laser-induced pressure propagation method

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Qi, Tianrun Ren, Hanwen Gao, Haoyu Li, Qingmin He, Xinjun Wang, Jian North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China Tokyo City University Measurement and Electric Machine Control Laboratory Setagaya158-8557 Japan

The crucial factor for ensuring space charge measurement resolution and signal intensity with the laser-induced pressure propagation method is the exceptional excitation performance. This study proposes a new approach for detecting high-frequency pressure pulse signals using the photoelastic effect. Based on the constructed pressure pulse optical detection system, the excitation characteristics and space charge measurement signals are investigated under different electrode materials and laser parameters. The experimental findings demonstrate that the selection of the target material, optimization of laser energy and spot diameter can significantly enhance the measurement resolution and signal intensity. Further, an optimized excitation scheme for the LIPP measurement device is proposed. The optimal spatial resolution and signal intensity are achieved when utilizing graphene as the target with laser energy of 340 mJ and spot diameter of 10 mm. Furthermore, the outcomes of the space charge evolution under intense electric field confirm the efficacy of the excitation optimization approach. Ultimately, the pressure pulse properties and generation process under distinct mechanisms are elucidated by integrating the developed simulation model of laser-induced pressure pulse. This study can offer valuable insights into the design of excitation scheme and investigation of the pressure wave generation mechanism in the LIPP method. © 1994-2012 IEEE.

关键词： Photoelasticity

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Non-unit protection for HVDC grids based on wavelet analysis of transient voltage

Non-unit protection for HVDC grids based on wavelet analysis...

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18th International Conference on Developments in power system Protection (DPSP APAC 2025)

作者： Xinyuan Li Boliang Jin Zhenkang Qi Binshu Chen Xinzhou Dong State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing People's Republic of China

ISBN: (数字)9781837242900

DC grids utilizing MMC-HVDC technology have been extensively researched and implemented in various locations, demonstrating substantial benefits in terms of system flexibility and renewable energy integration. Due to the rapid escalation of fault currents, DC line protections must quickly identify and isolate faults, typically within milliseconds, leading to the adoption of non-unit methods as the primary protection. This paper introduces a non-unit protection scheme that employs transient voltage at both the line-side and converter-side of the supplemental inductor. Based on a reduced model focusing on the boundary of a DC line, the transient process of the fault is examined using traveling wave theory, and the voltage characteristics at both ends of the supplemental inductor are scrutinized. The theoretical analysis also brings out a practical method for threshold-value calculation. Then, a protection criterion, based on wavelet analysis of the transient voltage, is established. This proposed method is capable of fulfilling the requirements for speed, selectivity and fault resistance and has been verified through PSCAD/EMTDC simulations.

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