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学者姓名：徐玉珍
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Abstract ：
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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A Y-source coupled inductor-based high voltage gain dc-dc converter with zero input current ripple (ZICR) is proposed in this article. ZICR is achieved by adding an auxiliary circuit which is composed of an inductor, a capacitor, and a coupled inductor in series. The presence of the capacitor results in an average zero current passing through the ZICR structure. Therefore, a small size magnetic core can be used to design the inductor, which can reduce the volume and copper loss of the magnetic components. In addition, a Y-source coupled inductor along with a voltage multiplier is used to achieve high voltage. The inherent passive clamp circuit effectively restricts the off-voltage spike and voltage stress across the switch. By utilizing capacitors to absorb the leakage energy, zero current switching (ZCS) turn-off of all diodes is achieved and mitigates the reverse-recovery issues of the converter. A 200 W 32-380 V 50 kHz experimental prototype is built to verify the feasibility of this converter. Within the output power range of 40-200 W, the efficiency of the prototype with the ZICR structure is higher than that without this structure. The maximum efficiency of the converter with the ZICR structure and without it are 95.9% and 95.6%, respectively.
Keyword ：
DC-DC converter DC-DC converter high voltage gain high voltage gain Y-source coupled inductor Y-source coupled inductor zero input current ripple (ZICR) zero input current ripple (ZICR)
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| GB/T 7714 | Xu, Yuzhen , Zhang, Yueling , Zhou, Mingzhu et al. Y-Source Coupled Inductor Based High Voltage Gain DC-DC Converter With Zero Input Current Ripple [J]. | IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS , 2025 , 13 (1) : 199-213 . |
| MLA | Xu, Yuzhen et al. "Y-Source Coupled Inductor Based High Voltage Gain DC-DC Converter With Zero Input Current Ripple" . | IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 13 . 1 (2025) : 199-213 . |
| APA | Xu, Yuzhen , Zhang, Yueling , Zhou, Mingzhu , Xie, Ronghuan , Mao, Xingkui , Chen, Xiaoying et al. Y-Source Coupled Inductor Based High Voltage Gain DC-DC Converter With Zero Input Current Ripple . | IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS , 2025 , 13 (1) , 199-213 . |
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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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Abstract ：
随着电动汽车的普及,充电基础设施需求急剧上升,迫切需要对充电桩进行维护和故障诊断.为有效利用不同尺度下的充电桩故障信号特征,该文提出一种基于多尺度卷积神经网络和双注意力机制的 V2G(vehicle-to-grid)充电桩开关管开路故障信息融合诊断方法.该方法基于卷积神经网络,引入自注意力机制突出故障信号中的重要特征.同时,使用最大池化层和平均池化层处理故障信号,提供不同尺度的互补信息;此外,引入通道注意力机制关注不同通道特征,可提高模型性能;最后,采用Softmax分类器进行分类和识别.仿真结果表明,该方法在多个方面优于其他对比算法,包括收敛速度、抑制过拟合以及诊断准确率等,并且表现出卓越的抗噪性能,能够有效应对充电桩故障信号中的噪声.在实际测试中,该方法实现了开关管开路故障位置的准确定位,其准确率达 96.67％.结果为充电桩开关管开路故障的诊断提供了可行的解决方案.
Keyword ：
信息融合 信息融合 充电桩 充电桩 故障诊断 故障诊断 注意力机制 注意力机制 深度学习 深度学习
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| GB/T 7714 | 徐玉珍 , 邹中华 , 刘宇龙 et al. 基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断 [J]. | 中国电机工程学报 , 2025 , 45 (8) : 2992-3002,中插12 . |
| MLA | 徐玉珍 et al. "基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断" . | 中国电机工程学报 45 . 8 (2025) : 2992-3002,中插12 . |
| APA | 徐玉珍 , 邹中华 , 刘宇龙 , 曾梓洋 , 文云 , 金涛 . 基于多尺度卷积神经网络和双注意力机制的V2G充电桩开关管开路故障信息融合诊断 . | 中国电机工程学报 , 2025 , 45 (8) , 2992-3002,中插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.
Keyword ：
Attention mechanism Attention mechanism Electricity price prediction Electricity price prediction Grey wolf optimization algorithm Grey wolf optimization algorithm Long-short term memory Long-short term memory 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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Abstract ：
随着便携式电子产品的轻薄化和小型化发展,电源模块也朝着小型化发展,所以对电源模块中体积最大的磁性元件电感进行建模设计就显得尤为重要.针对片式电感器的薄膜电感,首先应用磁场分割法,建立磁路模型,计算其感量;接着,进一步分析各磁阻的磁场大小,推出简易的磁场表达式,代入用多项式拟合后的磁导率曲线,对电感直流叠加特性进行研究;并且分析了当设计某目标感值时,匝数和电阻的关系,得出了在最优匝数下的磁芯高度占比范围在59％左右;最后运用有限元仿真软件验证了该模型的精确性和实用性,当磁芯高度占比在54.74％～63.16％时,误差范围为-1.97％～0.31％,当磁芯高度在59％左右时,饱和电流求解误差在4.28％以内.
Keyword ：
片式电感 片式电感 直流叠加特性 直流叠加特性 磁场分割法 磁场分割法 薄膜电感 薄膜电感
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| GB/T 7714 | 张聪 , 徐玉珍 . 片式薄膜功率电感的感值建模与结构分析 [J]. | 电气开关 , 2024 , 62 (1) : 31-39 . |
| MLA | 张聪 et al. "片式薄膜功率电感的感值建模与结构分析" . | 电气开关 62 . 1 (2024) : 31-39 . |
| APA | 张聪 , 徐玉珍 . 片式薄膜功率电感的感值建模与结构分析 . | 电气开关 , 2024 , 62 (1) , 31-39 . |
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目前,开关电源等电力电子设备主要的能量损耗集中在开关器件上的开关损耗,因此国内外学者对软开关技术进行了大量的深入研究,以期提高电力电子设备的效率.提出一种新型软开关双向大变比DC-DC变换器,通过软开关方案的设计与应用,减小了功率开关管电压尖峰,实现了各功率开关管相应的零电压开通与关断、零电流开通与关断,提升了变换器的效率.对所提变换器的工作原理和模态变化进行了详细的理论分析,并搭建了1台48～400 V、400 W实验样机,验证了理论分析的正确性.
Keyword ：
DC-DC变换器 DC-DC变换器 大变比 大变比 开关电源 开关电源 软开关 软开关
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| GB/T 7714 | 王亮 , 徐玉珍 . 一种新型软开关双向大变比DC-DC变换器研究 [J]. | 电源学报 , 2024 , 22 (z1) : 34-41 . |
| MLA | 王亮 et al. "一种新型软开关双向大变比DC-DC变换器研究" . | 电源学报 22 . z1 (2024) : 34-41 . |
| APA | 王亮 , 徐玉珍 . 一种新型软开关双向大变比DC-DC变换器研究 . | 电源学报 , 2024 , 22 (z1) , 34-41 . |
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针对1种无变压器、无耦合电感的新型单开关高增益变换器,通过将电压升举单元引入Boost变换器来实现在较小占空比情况下提高电压增益,降低开关管、二极管电压应力,减小开关管的导通损耗,从而提高变换器的效率.为了进一步提高变换器的动态性能与抗扰动能力,在分析单神经元控制器的基础上引入免疫反馈机制,研究模糊免疫-单神经元PID控制策略,将模糊免疫控制与单神经元智能控制器结合,实现了单神经元比例系数的自整定.最后,对所提变换器和控制策略进行了仿真研究,并设计了 1台输出200 V/0.5 A的实验样机进行实验验证,仿真和实验结果均表明:所提变换器可在较小占空比情况下获得较高电压增益;与传统的PID控制策略相比,所提模糊免疫-单神经元PID控制策略能更有效地抑制系统扰动,提高变换器的动态性能,具有更强的自适应能力和鲁棒性.
Keyword ：
低应力 低应力 单开关 单开关 单神经元PID控制器 单神经元PID控制器 模糊免疫自整定 模糊免疫自整定 高增益 高增益
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| GB/T 7714 | 万陆峰 , 徐玉珍 , 金涛 . 基于模糊免疫-单神经元PID控制单开关高增益直流变换器 [J]. | 电源学报 , 2024 , 22 (6) : 13-24 . |
| MLA | 万陆峰 et al. "基于模糊免疫-单神经元PID控制单开关高增益直流变换器" . | 电源学报 22 . 6 (2024) : 13-24 . |
| APA | 万陆峰 , 徐玉珍 , 金涛 . 基于模糊免疫-单神经元PID控制单开关高增益直流变换器 . | 电源学报 , 2024 , 22 (6) , 13-24 . |
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This paper introduces a novel single-switch converter that incorporates a three-winding coupled inductor, integrating the switched capacitor and the three-winding coupled inductor (TWCI) into a new boost unit to enhance its voltage gain. At the same time, a passive clamp circuit is utilized by the converter to reclaim energy from the leakage inductor and alleviate voltage stress on power switch S. Consequently, a power switch S featuring low forward voltage and reduced on-resistance may be chosen to minimize switching conduction loss in the converter. In addition, the proposed converter achieves high voltage gain under low turns ratio and low duty cycle conditions. Furthermore, it utilizes only one switch, resulting in a simple control scheme for the circuit. The principle of operation and steady state analysis of the proposed converter is described in detail and the design principles of the device are given. The relevant performance of the proposed converter is verified through simulation and the simulation results are given. © 2023 IEEE.
Keyword ：
Boost converter Boost converter Electric inductors Electric inductors Gain measurement Gain measurement Winding Winding
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| GB/T 7714 | Wen, Zhiyong , Zhang, Jiayang , Wen, Yun et al. A Novel High-Gain Converter Based on Three-Winding Coupled Inductor [C] . 2023 : 4134-4139 . |
| MLA | Wen, Zhiyong et al. "A Novel High-Gain Converter Based on Three-Winding Coupled Inductor" . (2023) : 4134-4139 . |
| APA | Wen, Zhiyong , Zhang, Jiayang , Wen, Yun , Wang, Weikun , Xu, Yuzhen , Jin, Tao . A Novel High-Gain Converter Based on Three-Winding Coupled Inductor . (2023) : 4134-4139 . |
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With the rapid proliferation of electric vehicles, the demand for charging infrastructure is surging. Consequently, it becomes imperative to address the issue of maintenance for charging pile faults. To harness the multi-scale characteristic present in fault signals more effectively, this paper proposed an information fusion diagnosis model based on an improved multi-scale convolutional neural network for the automated diagnosis of switch tubes open-circuit in charging piles. The approach conducts feature extraction at different scales on the fusion information from the original fault signals through a parallel branching structure. Furthermore, a channel attention mechanism is incorporated in the final layer of the network branch. This mechanism gives precedence to different channel features, dynamically assigning weights to the features obtained from distinct filters. This enhances fault-related features, suppresses ineffectual ones, and subsequently elevates the model's performance. Ultimately, the different scale features acquired from each network branch are merged and subjected to classification and recognition using a Softmax classifier. Simulation results underscore the effectiveness of the proposed method in diagnosing switch tube open-circuit faults in the charging pile. © 2023 IEEE.
Keyword ：
Charging (batteries) Charging (batteries) Convolution Convolution Convolutional neural networks Convolutional neural networks Electric vehicles Electric vehicles Failure analysis Failure analysis Fault detection Fault detection Information fusion Information fusion Network layers Network layers Piles Piles
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| GB/T 7714 | Zou, Zhonghua , Zeng, Ziyang , Wen, Yun et al. Information Fusion Model Based Improved Multi-Scale Convolutional Neural Network for Fault Diagnosis in EV V2G Charging Pile [C] . 2023 : 4068-4073 . |
| MLA | Zou, Zhonghua et al. "Information Fusion Model Based Improved Multi-Scale Convolutional Neural Network for Fault Diagnosis in EV V2G Charging Pile" . (2023) : 4068-4073 . |
| APA | Zou, Zhonghua , Zeng, Ziyang , Wen, Yun , Wang, Weikun , Xu, Yuzhen , Jin, Tao . Information Fusion Model Based Improved Multi-Scale Convolutional Neural Network for Fault Diagnosis in EV V2G Charging Pile . (2023) : 4068-4073 . |
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