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Measuring Near-Field Radiative Heat Transfer in a Graphene-SiC Heterostructure

Physical Review Applied 2023年第2期19卷 024019-024019页

作者： Naeem Iqbal Sen Zhang Shuai Wang Zezheng Fang Yaoyuan Hu Yongdi Dang Minjie Zhang Yi Jin Jianbin Xu Binfeng Ju Yungui Ma State Key Lab of Modern Optical Instrumentation Centre for Optical and Electromagnetic Research College of Optical Science and Engineering International Research Center for Advanced Photonics Zhejiang University Hangzhou China The State Key Lab of Fluid Power Transmission and Control School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang Province Key Laboratory of Quantum Technology and Device Department of Physics Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Road Hong Kong China

Near-field radiative heat transfer between closely placed objects may exceed the far-field black-body radiation limit by orders of magnitude, especially between polaritonic materials. Great efforts have been made to experimentally measure this fundamental effect in various systems. In this work, we manage to experimentally characterize the near-field thermal emission between less explored plasmon-phonon hybrid material systems made of a graphene-SiC heterostructure. The experiment is carried out using a custom-designed all-optical measurement setup. A heat flux enhancement factor of 26 over the black-body radiation limit is obtained at a 150-nm vacuum gap. Three fundamental modes, i.e., surface plasmon and phonon polaritons with frustrated modes, are identified to contribute the near-field heat flux in this hybrid system. The measured results are well reproduced from the analytical calculations, indicating the robustness of the measurement. Deeper experimental explorations for near-field heat transfer between more complicated material systems could be encouraged in the future.

关键词： Energy harvesting devices Heat transfer Near-field optics Surface plasmon polariton Graphene Van der Waals systems Optical techniques

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Research on the Influence of Terrain Curved-Surface on Lightning Parameters 4

Research on the Influence of Terrain Curved-Surface on Light...

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4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Zhao, Hailong Zhang, Dandan Pang, Songling Pan, Wenwu Yang, Liguo Wei, Shixun Electric Power Science Research Institute of Hainan Power Grid Co. Ltd. Hainan Power Grid Tropical Smart Grid Laboratory Haikou China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology Wuhan China

ISBN: (纸本)9781728171494

Lightning parameters are indispensable key parameters in lightning protection design. Based on the ground lightning data from the Hainan lightning location system in 2009, the relationships between lightning parameters (the average lightning current amplitude of total ground flashover, the proportion of positive and the negative flashover, and total ground flashover density) and terrain curved-surface parameters (slope, slope aspect, and slope position) are studied by means of GIS spatial analysis and mathematical statistics. Firstly, it is concluded that there is a cubic function relationship between the average lightning current amplitude of total ground flashover and slope. The proportion of positive flashover decreases with the increase of slope, and the distribution of total ground flashover density features U-shape with the increase of slope. The lightning protection should be strengthened for the transmission lines built on the mountain with the slope below 10° or above 48°. In addition, when the slope aspect is in the southwest direction, the average lightning current amplitude of total ground flashover and total ground flashover density are the lowest, while the proportion of positive flashover is the largest among all slope aspects. So some anti-backflashover measures should be taken for transmission lines built in the southwest slope aspect. Besides, the average lightning current amplitudes of total ground flashover are the largest and the total ground flashover densities are the highest in ridge and valley among all slope position. Some special lightning protection measures should be considered to be taken in ridge and valley. © 2021 IEEE.

关键词： Flashover

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Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

arXiv

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arXiv 2023年

作者： Ai, Xinkun Liu, Kun Zheng, Wei Fan, Yonggang G. Wu, Xinwu Zhang, Peilong Wang, LiYe Zhu, JanFeng Pan, Yuan State Key Laboratory of Advanced Electromagnetic Technology International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Wugang Resources Group Co. Ltd. Ezhou436000 China

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Research on key Geographic Factors Affecting Lightning Tripping Based on Association Rules and Entropy Weight 4

Research on Key Geographic Factors Affecting Lightning Tripp...

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4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Pang, Songling Zhang, Dandan Yang, Liguo Wei, Shixun Zhao, Hailong Pan, Wenwu Electric Power Science Research Institute of Hainan Power Grid Co. Ltd. Hainan Power Grid Tropical Smart Grid Laboratory Haikou China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology Wuhan China

ISBN: (纸本)9781728171494

Lightning is one of the important causes of transmission line tripping. In order to improve the effectiveness of transmission line risk assessment and provide reference for lightning protection design, it is necessary to study the key geographical factors affecting lightning tripping. In this paper, firstly, the geographical features such as altitude, slope, slope aspect, slope position and land type of the tower are extracted by using ArcGIS geographic software, and then the association rules are used to study the relationship between various geographical factors and lightning tripping. Finally, the entropy method is used to calculate the weight of various geographical factors on lightning tripping and determine the key factors. After calculation, the influence weight of slope on lightning tripping is 0.49, which accounts for the largest weight among geographical factors, so the influence of slope on lightning tripping should be considered in lightning protection design. © 2021 IEEE.

关键词： Association rules

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An intuitive and accurate EMI model for a modular multilevel converter

An intuitive and accurate EMI model for a modular multilevel...

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2020 IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Liu, Mingjie Liu, Jianben Wang, Xuehua State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Environmental Protection Wuhan China

ISBN: (纸本)9781728152240

Modular multilevel converter (MMC) has numerous sub-modules (SMs) that are complex electromagnetic interference (EMI) sources. It poses a challenge on the EMI analysis of MMC. Motivated by the challenge, this article presents an intuitive and accurate EMI model of MMC based on the superposition theorem. The stray parameters of MMC system are extracted to characterize the high-frequency characteristics of the circuit. By modeling the SMs as individual EMI sources, an EMI equivalent circuit is derived which can intuitively tell the conducted EMI principles of MMC. The EMI model based on the EMI equivalent circuit can accurately predict the EMI results of MMC without a great deal of calculation. Simulation results verify the effectiveness and validity of the proposed EMI model. © 2020 IEEE.

关键词： Equivalent circuits

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A Prediction Model for Time Series of Dissolved Gas Content in Transformer Oil Based on LSTM

A Prediction Model for Time Series of Dissolved Gas Content ...

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2020 International Conference on Ubiquitous Power Internet of Things, UPIOT 2020 and 4th International Symposium on Green Energy and Smart Grid, SGESG 2020

作者： Hu, Chuye Zhong, Yang Lu, Yiqi Luo, Xiaotong Wang, Shaorong State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan Hubei Province430074 China

By combining with dissolved gas analysis, time series prediction of dissolved gas content in oil provides a basis for transformer fault diagnose and early warning. In the view of that, a prediction model based on long short time memory (LSTM) network for time series of dissolved gas content in oil is proposed, which takes advantage of LSTM network's ability to deal with long-sequence prediction problems. Five characteristic gas concentrations are used as input to the model, and the hyper parameters of the model is optimized by Bayesian optimization algorithm to further improve prediction accuracy, then a LSTM prediction model is constructed. By case study, it is verified that the proposed model can precisely predict time series of dissolved gas content. Compared with gray model, BP neural network and support vector machine, the proposed model has higher prediction accuracy and can better track the trend of time series of dissolved gas content in oil. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Impurity radiation seeding of neoclassical tearing mode growth

arXiv

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arXiv 2024年

作者： Zeng, Shiyong Zhu, Ping Howell, Eric C. State Key Laboratory of Advanced Electromagnetic Engineering and Technology International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics School of Electrical and Electronic Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Nuclear Engineering and Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States Tech-X Corporation 5621 Arapahoe Avenue Suite A BoulderCO80303 United States

The physics of neoclassical tearing mode (NTM) is of great concern to the tokamak plasma stability and performance, especially in the burning plasma regime. Whereas a great deal about the different seeding mechanisms have been understood, and in many situations the seed event can be clearly identified, the potential seeding process of NTM due to the resistive tearing instability driven by the impurity radiation cooling still needs more studies. Recent NIMROD simulations have demonstrated that the local impurity radiation cooling can drive the seed island growth and trigger the subsequent onset of neoclassical tearing mode instability. The seed island is mainly driven by the local helical perturbation of the diamagnetic current induced by the perturbed pressure gradient as a result of the impurity radiative cooling on the rational surface. A heuristic closure for the neoclassical viscosity is adopted, and the seed island is further driven by the perturbed bootstrap current induced from the neoclassical electron viscous stress in the extended Ohm’s law. The growth rate of the NTM in simulations is found proportional to the electron neoclassical viscosity, and a theoretical neoclassical driving term is adopted to account for the nonlinear neoclassical island growth in the *** Codes 76W05 (Primary) © 2024, CC BY-NC-SA.

关键词： Tokamak devices

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Unveiling Bidding Uncertainties in Electricity Markets: A Bayesian Deep Learning Framework Based On Accurate Variational Inference

SSRN

引用

SSRN 2022年

作者： Wu, Shengyang Ding, Zhaohao Wang, Jingyu Shi, Dongyuan State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China School of Electrical and Electronic Engineering of North China Electric Power University Beijing China

With the ever-increasing level of bidding freedom bestowed to participants in deregulated electricity markets, bidding strategies have become more complicated, inevitably giving rise to the growth of market uncertainties. Despite the rich literatures on Bidding Behavior Forecasting (BBF), there remains a gap in enhancing the performance of probabilistic BBF and understanding the sources of bidding uncertainties in electricity markets. This paper proposes a holistic Bayesian Deep Learning (BDL) framework based on Accurate Variational Inference (AVI) to bridge the gap. The introduced framework first modifies the traditional Bayesian Neural Network (BNN) by introducing the parameter-selective Rényi's α-divergence to realize AVI. The ameliorated algorithm procures higher BBF accuracy. Afterwards, the proposed framework captures both aleatoric and epistemic uncertainty, two major uncertainties in deep learning that represent the uncertainties within bidding behaviors and within the proposed BBF model, respectively. A new metric called uncertainty contribution to evaluate the potential influencing factors' impact on the bidding uncertainties is then brought up. Using the proposed metric, we ascertain the prominent origin of bidding behavior uncertainty and propose realistic explanations to support the theory. Finally, numerical experiments are carried out utilizing historic data in Australia to demonstrate the performance of the proposed framework. © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Chronological Operation Simulation for Power System with Peak Regulation Ancillary Service Market in China: Formulation, Solving, and Validation

SSRN

引用

SSRN 2022年

作者： Wei, Lishen Ai, Xiaomeng Fang, Jiakun Lou, Yixuan Liao, Shiwu Wen, Jinyu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China TODE Co. Ltd. Nanjing210046 China

Peak regulation ancillary service market (PRSM) is an emerging and effective mechanism for renewable energy accommodation in China, especially under Carbon Neutral. To comprehensively analyze its long-term impact on the power system, a chronological operation simulation (COS) model is proposed. Besides, the computational-burden balancing technique based on the time-domain decomposition is developed to improve the computational efficiency of COS. The real-world northeast China power system with PRSM has been analyzed. Case studies validate the simulation with published statistical data and demonstrate the accuracy. Besides, 52.16% computation time is reduced compared to the widely used rolling horizon method. The necessity of PRSM for renewable energy accommodation is also verified through chronological simulation. The relationship between peak regulation costs and renewable energy penetration is also given, which demonstrates its applicability. © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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Real-time evaluation method for measurement error of capacitor voltage transformer 1

Real-time evaluation method for measurement error of capacit...

引用

1st IEEE China International Youth Conference on Electrical engineering, CIYCEE 2020

作者： Ma, Keqi Jiang, Tao Li, Hongbin Meng, Zhan Chen, Qing Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China

ISBN: (纸本)9781728196596

As one of the electrical parameters measuring devices, capacitor voltage transformer (CVT) is widely used in the grid with a voltage of 110kV and above. It provides reliable basis for electric energy measurement, state monitoring and relay protection, thus related to the safety, stability and economic operation of the power system. The existing measurement error evaluation methods are not conducive to timely evaluate the error of CVTs. Therefore, a real-time error evaluation method for CVTs based on same-phase voltage correlation and large-dimensional random matrices (SPVC-LDRM) is proposed. Collect same-phase voltage measurement data of CVTs, extract the error state characteristic parameters for CVTs based on the same-phase voltage correlation, and construct a large-dimensional random matrices model, then the evaluation index and its threshold are calculated to evaluate the error anomaly of CVTs in real time according to single-ring law. The simulation experiment verifies the effectiveness of the method in evaluating the measurement error of the CVTs. © 2020 IEEE.

关键词： Measurement errors

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