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Effects of nonlinear gradient index on radiative heat transfer in a one-dimensional medium by the DRESOR method

Journal of Physics: Conference Series 2012年第1期369卷

作者： Z C Wang Q Cheng G H Wang Z F Huang H C Zhou State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 Hubei People's Republic of China State Key Laboratory of Control & Simulation of Power System & Generation Equipment Department of Thermal Engineering Tsinghua University Beijing 10084 P. R. China To whom any correspondence should be addressed

The DRESOR (Distribution of Ratios of Energy Scattered by the medium Or Reflected by the boundary surface) method is applied for radiative heat transfer in a one-dimensional medium with a nonlinear gradient index and gray boundary surfaces. In this proposed method, the DRESOR values calculated by the Monte Carlo method express quantitatively the impact of scattering on radiative transfer and the radiative intensity with high directional resolution of high precision can be easily obtained. With given media characteristics and boundary conditions, the temperature and radiative flux distributions inside the medium are calculated under the condition of radiative equilibrium. It is shown, in the cases studied, that the DRESOR method has a good accuracy. The temperature distributions have a node with different kinds of sine changed gradient index distributions under the same boundary emissivity. The impact of the gradient index on the radiative heat transfer is considerable, and the same as that of the ratios of its amplitude and average index. Besides, the effects of optical thickness, boundary emissivity and scattering phase function on radiative transfer also should be paid adequate attention.

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Corrigendum to “Using fuzzy theory and information entropy for water quality assessment in three Gorges region, China” [Expert systems with Applications 37 (3) (2010) 2517–2521]

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Expert systems with Applications 2010年第6期37卷 4720-4720页

作者： Li Liu Jianzhong Zhou Xueli An Yongchuan Zhang Li Yang Dispatch & Communication Center Hunan Electric Power Company Hunan Province Changsha 410007 China College of Hydroelectric and Digitalization Engineering Huazhong University of Science and Technology Hubei Province 430074 Wuhan China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Thermal Engineering Tsinghua University Beijing 100084 China

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Polymeric insulating materials characteristics for high-voltage applications

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年 516-528页

作者： Tian-Yu Wang Gui-Xin Zhang Zhi-Min Dang Jie Mao Boya Zhang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China Department of Chemical and Biomolecular Engineering University of California Los Angeles CA USA School of Chemistry and Chemical Engineering State Key Laboratory of High-Efficiency Utilization of Coal and Green Chemical Engineering Ningxia University Yinchuan China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an China

Large-scale power system transmission and transformation equipment operates in high-voltage environments, placing notable requirements on the insulation strength of polymers used in core insulation frameworks. Increasing the polymer insulation performance in high-voltage environments is crucial for protecting large-capacity and integrated power equipment and ensuring the safe and stable operation of electrical devices. Here, we discuss the phenomena and the underlying mechanisms of bulk breakdown and surface flashover that lead to insulation failure in polymers and present relative strategies to increasing the performance of the insulating materials. We analyse the challenges faced by polymeric insulating materials in various extreme environments and the strategies to address them. Finally, we identify key challenges and highlight future directions and opportunities for the development of reliable polymeric insulating materials.

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Carbon Monitor-power: near-real-time monitoring of global power generation on hourly to daily scales

arXiv

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arXiv 2022年

作者： Zhu, Biqing Song, Xuanren Deng, Zhu Zhao, Wenli Huo, Da Sun, Taochun Ke, Piyu Cui, Duo Lu, Chenxi Zhong, Haiwang Hong, Chaopeng Qiu, Jian Davis, Steven J. Gentine, Pierre Ciais, Philippe Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Laboratoire des Sciences du Climate et de l'Environnement LSCE Orme de Merisiers Gifsur-Yvette91191 France Product and Solution & Website Business Unit Alibaba Cloud Zhejiang Hangzhou311121 China Department of Earth and Environmental Engineering Columbia University New YorkNY United States Department of Civil & Mineral Engineering University of Toronto TorontoONM5S 1A1 Canada Department of Electrical Engineering The State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and Global Governance Tsinghua University Beijing China Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Earth System Science University of California Irvine 3232 Croul Hall IrvineCA92697-3100 United States

We constructed a frequently updated, near-real-time global power generation dataset: Carbon Monitor-power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The Carbon Monitor-power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making. © 2022, CC BY.

关键词： Carbon

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Measurement-driven AA-CAES Model for Distribution Network Dispatch in Coordination with Renewable generation

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IOP Conference Series: Earth and Environmental Science 2020年第1期529卷

作者： Yuguang Xie Jiayu Bai Dangwu Liu Tianwen Zheng Shengwei Mei State Grid Anhui Electric Power Research Institute Hefei 230601 China State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering) Tsinghua University Beijing 100084 China State Grid Anhui Electric Power CO. LTD. Hefei 230022 China Sichuan Energy Internet Research Institute Tsinghua University Chengdu 610213 China

Advanced adiabatic compressed air energy storage (AA-CAES) could play an important role in power distribution system for mitigating the intermittency of distributed renewable generations and reducing the peak-valley difference of load demand, which requires to operate AA-CAES under off-design conditions. A power system dispatch oriented AA-CAES model was proposed based on the technically thermodynamic performance curves of compressor and expansion calibrated from experiments, highlighting the impact of AA-CAES part-lard features on charging/discharging efficiency and capacity. The proposed model is integrated with economic dispatch problem of power distribution systems, giving rise to a nonlinear program due to the state-dependent charging and discharging performances. The dispatch model is further linearized via piecewise linear approximation and special-ordered set of type 2, and its equivalent mixed integer linear formulation is presented. A modified IEEE 33-bus system is adopted to verify the effectiveness of proposed model for renewables accommodation.

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

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Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

作者： Jiaqi Qu Qiang Sun Zheng Qian Lu Wei Hamidreza Zareipour School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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A software and hardware design of intelligent measurement and control terminal of distribution network based on a communication adaptive approach

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IOP Conference Series: Earth and Environmental Science 2021年第2期651卷

作者： Yujie Ding Xiaobing Xiao Hao Tian Chang Liu Guangze Ouyang Ruifeng Zhang Anshun Power Supply Bureau of Guizhou Power Grid Co. Ltd. Anshun China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Dept. of Electrical Engineering Tsinghua University) Beijing China Binjiang College of Nanjing University of Information Science and Technology Wuxi China

With the development of science and technology, the hardware and software design of transformer intelligent measurement and control terminal is very important. Based on the hardware and software appization of internet of things, the design scheme of hardware and software structure is given. Supported by high-performance operating system, the terminal device can be upgraded flexibly through APP application software. The intelligent terminal pair can realize full data collection, full control and full fusion, and real-time exchange of key operation data with the power distribution master station. The intelligent terminal can support 5G communication and realize full compatibility of communication conditions downward.

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Lithium Metal Anodes: A Triple-Gradient Host for Long Cycling Lithium Metal Anodes at Ultrahigh Current Density (Small 30/2020)

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Small 2020年第30期16卷

作者： TrungHieu Le Qinghua Liang Ming Chen Ciqing Yang Zhihao Yu Jie Cheng Feiyu Kang Ying Yang State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua University Beijing 100084 China Department of Chemical Engineering The University of Melbourne Parkville VIC 3010 Australia Zhejiang Yuyuan Energy Storage Technology Co. Ltd. Huzhou 313100 China Laboratory of Advanced Materials Department of Materials Science and Engineering Tsinghua University Beijing 100084 China

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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic (vol 11, 5172, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 5172页

作者： Liu, Zhu Ciais, Philippe Deng, Zhu Lei, Ruixue Davis, Steven J. Feng, Sha Zheng, Bo Cui, Duo Dou, Xinyu Zhu, Biqing Guo, Rui Ke, Piyu Sun, Taochun Lu, Chenxi He, Pan Wang, Yuan Yue, Xu Wang, Yilong Lei, Yadong Zhou, Hao Cai, Zhaonan Wu, Yuhui Guo, Runtao Han, Tingxuan Xue, Jinjun Boucher, Olivier Boucher, Eulalie Chevallier, Frederic Tanaka, Katsumasa Wei, Yiming Zhong, Haiwang Kang, Chongqing Zhang, Ning Chen, Bin Xi, Fengming Liu, Miaomiao Breon, Francois-Marie Lu, Yonglong Zhang, Qiang Guan, Dabo Gong, Peng Kammen, Daniel M. He, Kebin Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232 Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Institut Pierre-Simon Laplace Sorbonne Université / CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France. Center for Energy and Environmental Policy Research Beijing Institute of Technology Beijing China. Department of Electrical Engineering the State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and G

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5

关键词： Climate sciences Atmospheric science Environmental sciences Environmental social sciences

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