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学者姓名：金涛
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为研究温度对油纸绝缘频域介电谱的影响,并探索高效的频温归一化策略以消除不同环境因素带来的测试温度误差,该文提出了基于极化复电容实部一阶微分解谱的多弛豫分解方法.首先,利用微分图谱特征划分出低频弛豫、中低频多弛豫、高频弛豫三类不同弛豫区间进行频温介电机理推演,发现各弛豫过程温度特性差异显著;其次,以Arrhenius衍生方程计算不同弛豫的活化能,基于该频温特性参量提取介质中多类贡献分量的频温频移因子,还原标准温度下的介电图谱;最后,利用不同温度及不同老化程度的试样验证该方法.实验分析表明,该方法很好地解决了传统频温归一法所存在的偏差,且对于不同老化程度的介质具有较好的适用性,可为现场测试提供可靠的理论支撑.
Keyword ：
弛豫活化能 弛豫活化能 微分解谱 微分解谱 油纸绝缘 油纸绝缘 频域介电法 频域介电法 频温归一化 频温归一化
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| GB/T 7714 | 邹阳 , 黄煜 , 方梦泓 et al. 基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究 [J]. | 电工技术学报 , 2025 , 40 (5) : 1575-1586 . |
| MLA | 邹阳 et al. "基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究" . | 电工技术学报 40 . 5 (2025) : 1575-1586 . |
| APA | 邹阳 , 黄煜 , 方梦泓 , 姚雨佳 , 金涛 . 基于微分解谱的油纸绝缘多弛豫频温机理与归一化研究 . | 电工技术学报 , 2025 , 40 (5) , 1575-1586 . |
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为解决传统有限控制集模型预测控制存在开关频率不固定的缺点,该文提出一种基于离散虚拟电压矢量的最优开关序列模型预测控制策略.所提策略采用开关序列的方式来固定开关频率,利用离散空间矢量的原理预定义虚拟电压矢量,引入一个查找表来描述虚拟电压矢量的开关序列占空比,并通过一种有效的寻优算法来减少控制策略的计算负担.仿真结果表明:所提控制策略在固定逆变器开关频率的同时,避免繁琐的权重系数整定过程,直流侧电容电压偏移控制在3%以内.
Keyword ：
三相三电平逆变器 三相三电平逆变器 开关频率 开关频率 模型预测控制 模型预测控制
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| GB/T 7714 | 朱敏龙 , 张煌辉 , 张杰梁 et al. 三电平逆变器固定开关频率的模型预测开关序列控制 [J]. | 中国测试 , 2025 , 51 (2) : 97-105 . |
| MLA | 朱敏龙 et al. "三电平逆变器固定开关频率的模型预测开关序列控制" . | 中国测试 51 . 2 (2025) : 97-105 . |
| APA | 朱敏龙 , 张煌辉 , 张杰梁 , 金涛 . 三电平逆变器固定开关频率的模型预测开关序列控制 . | 中国测试 , 2025 , 51 (2) , 97-105 . |
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为了解决城市轨道交通列车的启停过程和分布式能源进入轨道交通能源路由器导致直流母线功率波动严重的问题，需要在不同端口之间进行能量路由，涉及多种运行模式，而无缝切换是一个重大挑战。该文基于分层控制策略提出一种适用于城市轨道交通的六端口能源路由器拓扑结构以及分层协调控制策略，使多端口能源路由器能够在列车停车、加速、恒功率驱动和减速等工况下协调运行，并在各种工况下无缝切换。能源中央调度层通过采样各端口的状态信息发送工况指令，微电网控制层采用集中控制，接收上层工况指令，向各局部控制层发送驱动信号，接收微电网控制层的驱动信号。局部各端口控制器控制各变流器电路的开关动作，并将变流器的工作状态上传至微电网控制层，以准确传输所需的列车负载功率，保持直流母线电压的稳定。最后，采用Matlab/Simulink仿真和实验验证了适用于轨道交通的六端口能源路由器拓扑设计及分层协调控制策略的可行性。
Keyword ：
分层协调控制策略 分层协调控制策略 城市轨道交通 城市轨道交通 拓扑设计 拓扑设计 能源路由器 能源路由器
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| GB/T 7714 | 范鸿炜 , 张煌辉 , 崔宪阳 et al. 考虑储能和新能源的城市轨道交通能源路由器拓扑设计与控制策略研究 [J]. | 中国测试 , 2024 , 50 (10) : 1-11 . |
| MLA | 范鸿炜 et al. "考虑储能和新能源的城市轨道交通能源路由器拓扑设计与控制策略研究" . | 中国测试 50 . 10 (2024) : 1-11 . |
| APA | 范鸿炜 , 张煌辉 , 崔宪阳 , 金涛 . 考虑储能和新能源的城市轨道交通能源路由器拓扑设计与控制策略研究 . | 中国测试 , 2024 , 50 (10) , 1-11 . |
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The leading -edge technology of Digital Twin (DT) presents potential solutions for challenges associated with renewable energy resources (RER), particularly solar energy, such as optimal management, random nature and unpredictability, maintenance, security, and energy efficiency. These issues are more elevated today due to the widespread adoption of solar energy in the power system. A DT leverages advanced technologies including the Internet of Things (IoT), artificial intelligence, and computing techniques, to observe and confirm the state of physical entities, analyze data and derive valid information to monitor and optimize the entity 's operation. This Perspective strives to trace the growing body of advances in the field and proposes opportunity areas for DT of RER (RERDT) and DT of solar energy (DTSE) in various application domains including forecasting, reliability analysis, security, and resiliency. Barriers that hinder the adoption of RERDT and DTSE technologies to meet its continuous operation are discussed, and also possible views as future trends to deal with the challenges are presented. RERDT and DTSE technologies promise to be transformative in enabling flexible and sustainable energy sources as well as in active real-time management since it provides a full comprehension of the resources.
Keyword ：
Digital Twin Digital Twin Reliability Reliability Renewable energy resource Renewable energy resource Resiliency Resiliency Security Security Solar energy Solar energy
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| GB/T 7714 | Kavousi-Fard, Abdollah , Dabbaghjamanesh, Morteza , Jafari, Mina et al. Digital Twin for mitigating solar energy resources challenges: A Perspective [J]. | SOLAR ENERGY , 2024 , 274 . |
| MLA | Kavousi-Fard, Abdollah et al. "Digital Twin for mitigating solar energy resources challenges: A Perspective" . | SOLAR ENERGY 274 (2024) . |
| APA | Kavousi-Fard, Abdollah , Dabbaghjamanesh, Morteza , Jafari, Mina , Fotuhi-Firuzabad, Mahmud , Dong, Zhao Yang , Jin, Tao . Digital Twin for mitigating solar energy resources challenges: A Perspective . | SOLAR ENERGY , 2024 , 274 . |
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This paper addresses a secured co-dynamic model for the energy management of Electrical Vehicles (EVs) within the real community transportation system (RCTS). The proposed model aims to facilitate interoperability among mobile energy resources within the smart city, enabling the RCTS to model the co-dynamic-static transportation systems (TSs) simultaneously. The energy management model within the traffic flow system focuses on dynamic assignment, considering the power consumption associated with the density of moving vehicles. EVs play a key role in economically managing energy in both static and dynamic behaviors within charging stations while aligning with the current traffic flow. To enhance data security within the smart city ecosystem, a directed acyclic graph (DAG)-based decentralized cyber security approach is recommended. This approach ensures that data transactions involving mobile energy resources are secured against cyber-attacks through the use of public, private, and transaction blocks. Additionally, an uncertainty-based copula function is presented to create a precise management environment within the smart city. The results indicate that the proposed model for transportation energy resources tends to reduce energy costs by optimally controlling energy consumption within traffic flow, compared to normal conditions. © 2024 Elsevier Ltd
Keyword ：
Cybersecurity Cybersecurity Energy management Energy management Energy resources Energy resources Energy utilization Energy utilization Interoperability Interoperability Network security Network security Smart city Smart city Static analysis Static analysis
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| GB/T 7714 | Jafari, Mina , Kavousi-Fard, Abdollah , Sheikh, Morteza et al. A copula-based secured intelligent dynamic-static energy community transportation system for smart cities [J]. | Sustainable Cities and Society , 2024 , 107 . |
| MLA | Jafari, Mina et al. "A copula-based secured intelligent dynamic-static energy community transportation system for smart cities" . | Sustainable Cities and Society 107 (2024) . |
| APA | Jafari, Mina , Kavousi-Fard, Abdollah , Sheikh, Morteza , Jin, Tao , Karimi, Mazaher . A copula-based secured intelligent dynamic-static energy community transportation system for smart cities . | Sustainable Cities and Society , 2024 , 107 . |
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The combination therapy of magnetic hyperthermia and thermosensitive liposomes (TSL) is an emerging and effective cancer treatment method. The heat generation of magnetic nanoparticles (MNPs) due to an external alternating magnetic field can not only directly damage tumor cells, but also serves as a triggering factor for the release of doxorubicin from TSL. The aim of this study is to investigate the effects in the degree of tumor cell damage of two proposed injection strategies that consider intravenous administration. Since both MNPs and TSL enter the tumor region intravenously, this study establishes a biological geometric model based on an experiment-based vascular distribution. Furthermore, this study derives the flow velocity of interstitial fluid after coupling the pressure distribution inside blood vessels and the pressure distribution of interstitial fluid, which then provides the convective velocity for the calculation of subsequent nanoparticle concentration. Different injection strategies for the proposed approach are evaluated by drug delivery result, temperature distribution, and tumor cell damage. Simulation results demonstrate that the proposed delayed injection strategy after optimization can not only result in a wider distribution for MNPs and TSL due to the sufficient diffusion time, but also improves the distribution of the temperature and drug concentration fields for the overall efficacy of combination therapy. © 2024 Chinese Physical Society and IOP Publishing Ltd.
Keyword ：
injection strategy injection strategy intravenous injection intravenous injection magnetic hyperthermia magnetic hyperthermia temperature-sensitive liposomes temperature-sensitive liposomes
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| GB/T 7714 | Zhu, J. , Tang, Y. , Flesch, R.C.C. et al. Effect of different injection strategies considering intravenous injection on combination therapy of magnetic hyperthermia and thermosensitive liposomes [J]. | Chinese Physics B , 2024 , 33 (12) . |
| MLA | Zhu, J. et al. "Effect of different injection strategies considering intravenous injection on combination therapy of magnetic hyperthermia and thermosensitive liposomes" . | Chinese Physics B 33 . 12 (2024) . |
| APA | Zhu, J. , Tang, Y. , Flesch, R.C.C. , Jin, T. . Effect of different injection strategies considering intravenous injection on combination therapy of magnetic hyperthermia and thermosensitive liposomes . | Chinese Physics B , 2024 , 33 (12) . |
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Electricity price prediction is essential for the optimal dispatch in power markets, with accurate prediction models being critical for efficient power system operations and market trading decisions. Deep learning networks, with their powerful nonlinear modeling capabilities, have shown promising results in electricity price forecasting. However, their design techniques, especially the selection of network parameters, remain challenging. This indicates that the optimization and exploration of deep learning networks in electricity price forecasting models require further investigation. This paper innovatively proposes a forecasting model that uniquely integrates Variational Mode Decomposition (VMD), Grey Wolf Optimization (GWO), Attention Mechanism (ATT), and Long Short-Term Memory Network (LSTM), optimizing the model from three different perspectives. First, during the data preprocessing phase, the training set is subjected to VMD to reduce noise, thereby enhancing the capture of multi-scale characteristics inherent in electricity price time series. The ATT layer is integrated to adaptively allocate weights, enhancing the model's focus on significant features. The GWO is applied to optimize hyperparameters of the LSTM, accelerating convergence and improving iteration accuracy, thereby reducing model error. A series of experiments were conducted using multiple regional electricity price datasets, evaluated with several metrics including RMSE. The results validated the effectiveness of the proposed three modules in improving the performance of the time series network, with VMD making the most significant contribution. Among all models, VMD-GWO-ATT-LSTM consistently outperformed others, demonstrating its effectiveness and robustness in electricity price forecasting. © 2024 Elsevier Ltd
Keyword ：
Prediction models Prediction models Variational mode decomposition Variational mode decomposition
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| GB/T 7714 | Xu, Yuzhen , Huang, Xin , Zheng, Xidong et al. VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy [J]. | Renewable Energy , 2024 , 236 . |
| MLA | Xu, Yuzhen et al. "VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy" . | Renewable Energy 236 (2024) . |
| APA | Xu, Yuzhen , Huang, Xin , Zheng, Xidong , Zeng, Ziyang , Jin, Tao . VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy . | Renewable Energy , 2024 , 236 . |
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In order to solve the problem of insufficient boost capacity of high boost non-isolated converters required in renewable energy system, a soft-switched high voltage gain quasi-YZ source DC-DC converter is proposed based on the advantages of Y source converter and quasi-Z source converter. The proposed converter can not only improve the voltage boost capacity, but also effectively absorb voltage spikes caused by leakage inductor. Furthermore, the application of synchronous rectification technique enables the switches and diodes to operate in soft switching condition and improves the conversion efficiency. In addition, the proposed converter has continuous input current and low current ripple due to the single inductor connected at the input voltage. Moreover, it has the advantages of low switch voltage stress and common ground between input and output voltages. First, the working modes and steady states of the proposed converter are analyzed. Then, efficiency and loss distribution are calculated quantitatively. The performance of the proposed converter is compared with other converters, and the design requirements are defined. Finally, the effectiveness of the proposed converter is verified by a prototype made in the laboratory. ©2024 Chin.Soc.for Elec.Eng.
Keyword ：
DC-DC converter DC-DC converter high voltage gain high voltage gain quasi-Z source converter quasi-Z source converter soft switching soft switching Y source converter Y source converter
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| GB/T 7714 | Li, H. , Yan, X. , Weng, Y. et al. Soft-switched High Voltage Gain Quasi-YZ Source DC-DC Converter; [软开关高增益准 YZ 源 DC-DC 变换器] [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (11) : 4435-4445 . |
| MLA | Li, H. et al. "Soft-switched High Voltage Gain Quasi-YZ Source DC-DC Converter; [软开关高增益准 YZ 源 DC-DC 变换器]" . | Proceedings of the Chinese Society of Electrical Engineering 44 . 11 (2024) : 4435-4445 . |
| APA | Li, H. , Yan, X. , Weng, Y. , Lin, J. , Jin, T. . Soft-switched High Voltage Gain Quasi-YZ Source DC-DC Converter; [软开关高增益准 YZ 源 DC-DC 变换器] . | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (11) , 4435-4445 . |
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As the penetration of renewable energy increases rapidly, the power quality disturbance (PQD) is becoming more and more complex, making it difficult for traditional methods to accurately identify the PQD and locate the time interval. To address this problem, this paper proposes a PQD point classification and time interval identification method based on the incorporation of multi-level attention mechanism. The classification model is constructed by using convolutional neural network (CNN) with the local feature attention mechanism (LFAM) and the dual-scale attention mechanism (DSAM). LFAM tracks changes in amplitude by analyzing the envelope and selectively amplifies local features in the signal waveform using weighted techniques. On the other hand, DSAM facilitates the model in identifying the significance of features from both the channel and neuron perspectives. Finally, each sampling point is classified in the form of multiclass-multioutput, based on which the time interval is also identified. To validate the effectiveness of the proposed method, a simulation dataset with 63 PQD types is established. The average classification accuracy of the proposed model is 99.10% in a 30dB white noise environment, and the time-detection errors are all in the millisecond range, which has better generalization performance and robustness than other deep learning models. Additionally, a hardware platform utilizing an AC power supply is developed to assess the performance of the model. The model achieves an average accuracy of 99.03% on this platform, further verifying the reliability of the proposed method. ©2024 Chin.Soc.for Elec.Eng.
Keyword ：
attention mechanism attention mechanism deep learning deep learning fusion model fusion model point classification point classification power quality disturbance (PQD) power quality disturbance (PQD) time interval identification time interval identification
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| GB/T 7714 | Liu, Y. , Cui, X. , Yuan, D. et al. Research on Multi-level Attention Mechanism Optimized Method for Point Classification and Time Interval Identification of Power Quality Disturbances; [基于多级注意力机制融合的电能质量扰动点分类及时间定位方法研究] [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (11) : 4298-4310 . |
| MLA | Liu, Y. et al. "Research on Multi-level Attention Mechanism Optimized Method for Point Classification and Time Interval Identification of Power Quality Disturbances; [基于多级注意力机制融合的电能质量扰动点分类及时间定位方法研究]" . | Proceedings of the Chinese Society of Electrical Engineering 44 . 11 (2024) : 4298-4310 . |
| APA | Liu, Y. , Cui, X. , Yuan, D. , Jin, T. . Research on Multi-level Attention Mechanism Optimized Method for Point Classification and Time Interval Identification of Power Quality Disturbances; [基于多级注意力机制融合的电能质量扰动点分类及时间定位方法研究] . | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (11) , 4298-4310 . |
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In this article, a low-computational burden model predictive flux control (MPFC) based on discrete space vector modulation (DSVM) and the optimal switching sequence (OSS) is proposed for achieving switching frequency (SF) and computational burden efficiencies in motor drives fed by two-level voltage source inverter. The DSVM is used to extend the prediction candidates of MPFC and greatly improve the performance of the controller. A generalized minimum flux error method independent of the number of virtual vectors is derived to cancel the exhaustive optimization method and lower the execution time of the proposed algorithm. In addition, new overmodulation and OSS schemes are designed to optimize the use of dc-link voltage andmitigate the inverter SF when implementing the optimal control action into switching states. The comparative experimental results show that without significant performance degradation, the proposed strategy provided about 50% SF and 25% execution time reductions compared to the classic MPFC methods.
Keyword ：
Discrete space vector modulation (DSVM) Discrete space vector modulation (DSVM) low-complexity low-complexity model predictive flux control (MPFC) model predictive flux control (MPFC) optimal switching sequence (OSS) optimal switching sequence (OSS) switching frequency (SF) switching frequency (SF)
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| GB/T 7714 | Jin, Tao , Song, Huiqing , Ipoum-Ngome, Paul Gistain et al. Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 , 71 (1) : 305-315 . |
| MLA | Jin, Tao et al. "Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 71 . 1 (2024) : 305-315 . |
| APA | Jin, Tao , Song, Huiqing , Ipoum-Ngome, Paul Gistain , Mon-Nzongo, Daniel Legrand , Tang, Jinquan , Zhu, Minlong et al. Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 , 71 (1) , 305-315 . |
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