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学者姓名：金涛
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磁粒子成像(MPI)利用检测装置感应可视场内不同位置处磁纳米粒子(MNPs)的非线性磁化响应,并基于所获得的检测信号重建MNPs浓度分布.该文基于激励与接收线圈磁场强度对检测信号的影响,论证了X-space和投影重建成像算法中MPI检测装置磁场均匀性的重要性,继而提出一种由具有较高磁场均匀性的正方形亥姆霍兹线圈所构成的改进开放式检测装置.此外,在零磁场点和零磁场线扫描移动两种情况下,基于不同装置下均匀分布MNPs所获得的检测信号,评估了两种传统开放式检测装置和所提改进装置的检测效果.研究结果表明,所提改进开放式装置的检测结果相比两种传统开放式装置显著接近理想情况,进而也证实了检测装置磁场均匀性的重要性.此外,该文还发现在改进装置基础上采用二次谐波检测方法相较于三次谐波检测,可获得更佳的检测效果.
Keyword ：
开放式检测装置 开放式检测装置 检测信号 检测信号 正方形亥姆霍兹线圈 正方形亥姆霍兹线圈 磁场均匀性 磁场均匀性 磁粒子成像 磁粒子成像
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| GB/T 7714 | 汤云东 , 丁宇彬 , 金涛 . 考虑磁场均匀性优化的开放式磁粒子成像检测装置改进方法 [J]. | 电工技术学报 , 2025 , 40 (6) : 1718-1728 . |
| MLA | 汤云东 等. "考虑磁场均匀性优化的开放式磁粒子成像检测装置改进方法" . | 电工技术学报 40 . 6 (2025) : 1718-1728 . |
| APA | 汤云东 , 丁宇彬 , 金涛 . 考虑磁场均匀性优化的开放式磁粒子成像检测装置改进方法 . | 电工技术学报 , 2025 , 40 (6) , 1718-1728 . |
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With the growing adoption of electric vehicles, demand for charging infrastructure has increased significantly, highlighting the need for timely maintenance and fault diagnosis of charging piles. To effectively leverage multi-scale features in charging pile fault signals, this paper proposes a fault information fusion diagnosis method for vehicle-to-grid (V2G) charging piles with open-circuit switching tubes, based on a multi-scale convolutional neural network (CNN) and dual-attention mechanism. The approach builds upon CNNs by integrating a self-attention mechanism to emphasize critical fault signal features. Simultaneously, max pooling and average pooling layers process fault signals to extract complementary multi-scale information. Additionally, a channel attention mechanism is incorporated to enhance model performance by weighting different channel features. Fault classification is performed using a Softmax classifier. Simulation results demonstrate the method's superiority over other algorithms in convergence speed, overfitting suppression, and diagnostic accuracy, while exhibiting strong noise robustness—effectively handling noise interference in fault signals. Experimental tests show the method achieves 96.67% accuracy in locating open-circuit faults in switching tubes, providing an effective solution for diagnosing such faults in charging piles. ©2025 Chin.Soc.for Elec.Eng.
Keyword ：
Convolutional neural networks Convolutional neural networks Data fusion Data fusion Electric fault location Electric fault location Fault detection Fault detection Multilayer neural networks Multilayer neural networks Scales (weighing instruments) Scales (weighing instruments) Vehicle-to-grid Vehicle-to-grid
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| GB/T 7714 | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong et al. Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (8) : 2992-3002 . |
| MLA | Xu, Yuzhen et al. "Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism" . | Proceedings of the Chinese Society of Electrical Engineering 45 . 8 (2025) : 2992-3002 . |
| APA | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong , Zeng, Ziyang , Wen, Yun , Jin, Tao . Information Fusion Diagnosis of Switching Tube Open-circuit Fault in V2G Charging Piles Based on Multi-scale Convolutional Neural Network and Dual-attention Mechanism . | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (8) , 2992-3002 . |
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Electricity theft can result in attacks and tampering with advanced metering infrastructure. Although electricity theft has decreased with the widespread adoption of smart meters, significantly increasing the amount of measured data, the issue persists. This paper presents a novel method termed channel-correlation exploited hierarchical kernel network to address the problem of electricity theft detection, integrating matrix completion and channel optimization techniques. Initially, the proposed method addresses the issue of missing or abnormal original data by employing the alternating direction method of multipliers, enhancing the quality of data samples for training purposes. Subsequently, the Hierarchical Kernel Network utilizes different kernel sizes to extract diverse feature sets, thereby capturing comprehensive information to improve recognition accuracy. Furthermore, leveraging the channel correlation exploitation module of the compression and excitation network, the network effectively analyzes and learns the unique features of each channel, significantly enhancing classification performance. Through ablation studies and experiments conducted with varying proportions of missing data and different fraud rates, the proposed model consistently demonstrates superior performance across all metrics compared to other models. The practicality and effectiveness of the model are further validated through implementation on hardware platforms. These findings provide robust evidence of the model's efficacy and superiority in detecting electricity theft. © 2025 Elsevier Ltd
Keyword ：
Deep learning Deep learning Smart meters Smart meters
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| GB/T 7714 | Jin, Tao , Wang, Wanhao , Liu, Yulong et al. Advanced electricity theft detection in smart metering systems via Channel-Correlation enhanced hierarchical kernel networks and matrix completion [J]. | Measurement: Journal of the International Measurement Confederation , 2025 , 253 . |
| MLA | Jin, Tao et al. "Advanced electricity theft detection in smart metering systems via Channel-Correlation enhanced hierarchical kernel networks and matrix completion" . | Measurement: Journal of the International Measurement Confederation 253 (2025) . |
| APA | Jin, Tao , Wang, Wanhao , Liu, Yulong , Huang, Qinyu , Mohamed, Mohamed A. . Advanced electricity theft detection in smart metering systems via Channel-Correlation enhanced hierarchical kernel networks and matrix completion . | Measurement: Journal of the International Measurement Confederation , 2025 , 253 . |
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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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With electric vehicles' popularity, a surge has been created in demand for charging infrastructure. As a result, the maintenance of charging piles has become a critical issue that requires attention. To effectively utilize the fault features of the front and back circuits in case of the charging pile fails, a multifeature fusion model is proposed in this article. First, use the front- and back-stage feature information fusion module to fuse the collected front-stage fault feature quantity signals and the back-stage fault feature quantity signals. Then, the spatial and temporal feature extraction modules are used to mine the spatial and temporal high-dimensional features in parallel. Finally, through the spatiotemporal feature fusion classification module, the spatial and temporal features are fused and classified to achieve the purpose of fault diagnosis. The proposed method employs deep learning techniques, which avoids the cumbersome steps involved in graphical input and the errors arising from manually selecting features in traditional deep learning algorithms and gives full play to the parallel diagnostic performance of deep learning. The simulation results demonstrate that the proposed method outperforms other comparative algorithms in terms of diagnostic accuracy, convergence speed, and overfitting suppression, and has excellent noise immunity, which can cope with the noisy situation of charging piles. In the experimental test, the fault diagnosis accuracy of this method reached 96.36%, and its recognition sensitivity for most fault categories was higher than that of the comparison model, which further verified the superiority and robustness of this method.
Keyword ：
Capacitors Capacitors Charging pile Charging pile Circuit faults Circuit faults data fusion data fusion deep learning deep learning Deep learning Deep learning fault diagnosis fault diagnosis Fault diagnosis Fault diagnosis Feature extraction Feature extraction Integrated circuit modeling Integrated circuit modeling Rectifiers Rectifiers spatiotemporal features spatiotemporal features
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| GB/T 7714 | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong et al. Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) : 2243-2254 . |
| MLA | Xu, Yuzhen et al. "Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 11 . 1 (2025) : 2243-2254 . |
| APA | Xu, Yuzhen , Zou, Zhonghua , Liu, Yulong , Zeng, Ziyang , Zhou, Sheng , Jin, Tao . Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) , 2243-2254 . |
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To support higher voltage onboard power supply system and electric vehicle (EV) endurance scheme, reconfiguration on novel unbalance levels strategy is developed and adopted in a three-level bidirectional inductor-inductor-capacitor (LLC) resonant converter for wider voltage charging between supercapacitor energy storage (SCES) and battery energy storage (BES) in hybrid energy storage system (HESS), which the proposed converter is composed of an LLC resonant tank module (RTM) and two three-level coupling cascaded neutral point clamping active bridge (3L-CCNPC). Also, the definition of each voltage gain mode in forward and backward workings is established by hybrid modulation strategy with flexible multilevel output ability of novel active bridge. Besides, a wider range of the unified bidirectional voltage gain is achieved by switching among multiple voltage gain modes, which can be implemented by moving among unified bidirectional voltage gain points. Based on the optimization objectives, such as narrowing the pulse frequency modulation (PFM) variation range, the parameters of LLC RTM are designed. Finally, the results from built experimental prototype can verify that the obtained voltage gains are all close to the theoretical design values, and the key waveforms visually reflect the characteristics of novel unbalance levels strategy adopted in bidirectional voltage gain modes of each gain point.
Keyword ：
Bidirectional voltage gain modes Bidirectional voltage gain modes Bridge circuits Bridge circuits Clamps Clamps Couplings Couplings hybrid energy storage system (HESS) hybrid energy storage system (HESS) Inductance Inductance inductor-inductor-capacitor (LLC) resonant tank module (RTM) inductor-inductor-capacitor (LLC) resonant tank module (RTM) Modulation Modulation novel active bridge novel active bridge Resonant converters Resonant converters Switches Switches Topology Topology unbalance levels strategy unbalance levels strategy Voltage Voltage Voltage control Voltage control
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| GB/T 7714 | Zhang, Zhongyi , Xu, Yi , Yuan, Yisheng et al. Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) : 4906-4919 . |
| MLA | Zhang, Zhongyi et al. "Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 11 . 1 (2025) : 4906-4919 . |
| APA | Zhang, Zhongyi , Xu, Yi , Yuan, Yisheng , Cao, Hui , Liu, Peng , Jin, Tao . Reconfiguration on Novel Unbalance Levels Strategy Adopted in a Three-Level Bidirectional LLC Resonant Converter in HESS to EV Endurance Scheme . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2025 , 11 (1) , 4906-4919 . |
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A high-boost interleaved DC-DC converter that utilizes coupled inductors and voltage multiplier cells (VMC) is proposed in this paper. The input power supply connects to switches through the primary sides of two coupling inductors with an interleaved structure, which reduces the voltage stresses of the switches and lowers the input current ripple. Two capacitors and a diode are placed in series on the secondary side of the coupled inductors to enhance the high boost capability. The implementation of maximum power point tracking (MPPT) is facilitated by the simplification of the control system through common ground. To verify the effectiveness of the proposed converter, an experimental platform and a prototype based on a turns ratio of 1 are presented. The test results show that the voltage stresses on the switches are only 1/8 of the output voltage. The operating principle and design guidelines of the proposed converter are described in detail. The experimental results show that the converter is efficient and stable over a wide power range.
Keyword ：
Capacitors Capacitors Control systems Control systems coupled inductor coupled inductor DC-DC converter DC-DC converter DC-DC power converters DC-DC power converters High voltage gain High voltage gain High-voltage techniques High-voltage techniques Inductors Inductors low voltage stress low voltage stress Maximum power point trackers Maximum power point trackers Power system stability Power system stability Renewable energy sources Renewable energy sources Stress Stress Voltage control Voltage control
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| GB/T 7714 | Chen, Yin , Li, Haibin , Jin, Tao . A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems [J]. | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2025 , 10 (1) : 132-147 . |
| MLA | Chen, Yin et al. "A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems" . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 10 . 1 (2025) : 132-147 . |
| APA | Chen, Yin , Li, Haibin , Jin, Tao . A Novel High-Boost Interleaved DC-DC Converter for Renewable Energy Systems . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2025 , 10 (1) , 132-147 . |
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Effective analysis and classification of operational events in distribution networks (DNs), particularly those involving photovoltaic (PV) systems and electric vehicle charging stations (EVCSs), are essential for mitigating potential disturbances. This paper introduces a robust ensemble framework designed for power quality disturbance (PQD) analysis and event classification within DNs. The methodology begins with an enhanced empirical wavelet transform (EEWT), which incorporates spectral trends and window functions to accurately decompose PQDs caused by various events. These decomposed signals are then analyzed for amplitude and frequency characteristics using a mean sliding window-improved Hilbert transform (IHT). Based on these decompositions and inherent periodic features, a scale and cycle-based feature set, including time-dependent spectral features (TDSF), is formulated to differentiate between events. This feature set is subsequently classified using a light gradient boosting machine (LightGBM) to ensure precise event identification. The proposed approach is validated on a modified IEEE 13-node DN integrated with PV systems and EVCSs, simulating scenarios such as synchronization, outages and islanding. Under various noise conditions, the average accuracy of event identification reaches 99.33%, significantly outperforming other benchmark methods. Furthermore, the method's effectiveness is verified through real-time hardware-in-the-loop simulation, achieving an event identification accuracy of 98.33%. The results demonstrate that the proposed framework exhibits enhanced robustness and lower computational complexity compared to existing state-of-the-art methods.
Keyword ：
Distribution networks Distribution networks Empirical wavelet transform Empirical wavelet transform LightGBM LightGBM Power quality disturbances Power quality disturbances Time-dependent spectral feature Time-dependent spectral feature
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| GB/T 7714 | Liu, Yulong , Jin, Tao , Mohamed, Mohamed A. . Machine learning-based ensemble framework for event identification and power quality disturbance analysis in PV-EV distribution networks [J]. | ELECTRICAL ENGINEERING , 2025 . |
| MLA | Liu, Yulong et al. "Machine learning-based ensemble framework for event identification and power quality disturbance analysis in PV-EV distribution networks" . | ELECTRICAL ENGINEERING (2025) . |
| APA | Liu, Yulong , Jin, Tao , Mohamed, Mohamed A. . Machine learning-based ensemble framework for event identification and power quality disturbance analysis in PV-EV distribution networks . | ELECTRICAL ENGINEERING , 2025 . |
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Wind energy plays a significant role in sustainable power generation in power systems such as energy hubs, microgrids, smart grids and smart cities. On the other hand, some challenges such as wake effects in wind farms can lead to reduced efficiency and increased maintenance costs for the wind farms. This paper presents a cutting-edge approach to tackle these challenges through the development of a novel deep learning-based digital twin model. The proposed model integrates advanced deep learning algorithms with digital twin technology to accurately simulate and predict wake effects within wind farms. By leveraging data from various sensors and weather forecasts, the model can dynamically adjust turbine settings and optimize energy production in real-time. Key features of the digital twin include a convolutional neural network (CNN) for spatial analysis of wake patterns, a recurrent neural network (RNN) for temporal modelling of wind behaviour, and a reinforcement learning (RL) framework for autonomous decision-making. Through extensive simulations and validation against field data, the model demonstrates superior performance in mitigating wake effects and improving overall wind farm efficiency. This research contributes to the advancement of renewable energy technologies by providing a robust and scalable solution for optimizing wind farm operations and maximizing energy output. © 2025 Elsevier Ltd
Keyword ：
Wind forecasting Wind forecasting Windmill Windmill
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| GB/T 7714 | Kavousi-Fard, Abdollah , Dabbaghjamanesh, Morteza , Sheikh, Morteza et al. A novel deep learning based digital twin model for mitigating wake effects in wind farms [J]. | Renewable Energy Focus , 2025 , 53 . |
| MLA | Kavousi-Fard, Abdollah et al. "A novel deep learning based digital twin model for mitigating wake effects in wind farms" . | Renewable Energy Focus 53 (2025) . |
| APA | Kavousi-Fard, Abdollah , Dabbaghjamanesh, Morteza , Sheikh, Morteza , Jin, Tao . A novel deep learning based digital twin model for mitigating wake effects in wind farms . | Renewable Energy Focus , 2025 , 53 . |
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