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学者姓名：杨明发
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Abstract ：
提出一种新型的高升压DC-DC变换器.该变换器输入侧采用Boost结构,因此继承了输入电流连续的优点,适合用于可再生能源应用.变换器后级利用三绕组耦合电感和倍压单元进行集成,从而能够使用较小总匝比的耦合电感获得高电压增益,且提高电压增益的调节自由度.开关管电压应力低,可使用低耐压器件.此外,钳位支路回收储存在耦合电感漏感中的能量,从而提高效率.该文对变换器的工作模态进行详细讨论,并与其他变换器的性能进行对比.最后,搭建一台实验样机进行验证,实验获取的数据结果与理论层面的分析高度契合.
Keyword ：
DC-DC变换器 DC-DC变换器 可再生能源 可再生能源 增益调节 增益调节 耦合电路 耦合电路 连续输入电流 连续输入电流
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| GB/T 7714 | 杨明发 , 翁雨森 , 李海滨 et al. 集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器 [J]. | 太阳能学报 , 2025 , 46 (4) : 133-142 . |
| MLA | 杨明发 et al. "集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器" . | 太阳能学报 46 . 4 (2025) : 133-142 . |
| APA | 杨明发 , 翁雨森 , 李海滨 , 颜胥 , 林佳奇 , 金涛 . 集成三绕组耦合电感和倍压单元的高增益连续输入电流DC-DC变换器 . | 太阳能学报 , 2025 , 46 (4) , 133-142 . |
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This article puts forward a brand-new high-boost DC-DC converter. The converter adopts a Boost structure on the input side, inheriting the advantages of continuous input current, making it besuitable for renewable energy applications. The rear stage of the converter integrates the three-winding coupled inductor and the voltage double unit. As a result, a high voltage gain can be achieved by using a coupled inductor with a relatively small total turn ratio, and the degree of freedom in adjusting the voltage gain is also improved. The low switch voltage stress allows the use of low voltage rating devices. Additionally, the clamping circuit reutilizes the energy stored in the leakage inductance of the coupled inductor, thereby enhancing the efficiency. The article discusses the operating modes of the converter in detail and compares its performance with other converters. Finally, an experimental prototype is built for verification, and the experimental results match the theoretical analysis. © 2025 Science Press. All rights reserved.
Keyword ：
Coupled circuits Coupled circuits Solar fuels Solar fuels
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| GB/T 7714 | Mingfa, Yang , Yusen, Weng , Haibin, Li et al. HIGH GAIN CONTINUOUS INPUT CURRENT DC-DC CONVERTER WITH INTEGRATED TRIPLE-WINDING COUPLED INDUCTOR AND VOLTAGE DOUBLING UNIT [J]. | Acta Energiae Solaris Sinica , 2025 , 46 (4) : 133-142 . |
| MLA | Mingfa, Yang et al. "HIGH GAIN CONTINUOUS INPUT CURRENT DC-DC CONVERTER WITH INTEGRATED TRIPLE-WINDING COUPLED INDUCTOR AND VOLTAGE DOUBLING UNIT" . | Acta Energiae Solaris Sinica 46 . 4 (2025) : 133-142 . |
| APA | Mingfa, Yang , Yusen, Weng , Haibin, Li , Xu, Yan , Jiaqi, Lin , Tao, Jin . HIGH GAIN CONTINUOUS INPUT CURRENT DC-DC CONVERTER WITH INTEGRATED TRIPLE-WINDING COUPLED INDUCTOR AND VOLTAGE DOUBLING UNIT . | Acta Energiae Solaris Sinica , 2025 , 46 (4) , 133-142 . |
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Electricity theft causes substantial economic losses and safety hazards. While the widespread adoption of advanced metering infrastructure has significantly reduced electricity theft, perpetrators continue to find ways to exploit the system, employing increasingly covert and intricate methods. To address the ongoing challenge, this paper proposes an attention mechanism optimized multi-temporal granularity feature driven convolutional ensemble model for enhanced accuracy and robustness in electricity theft detection (ETD). For comprehensive feature extraction across diverse temporal scales, the proposed framework integrates two specialized feature extraction modules. The first module, a squeeze-and-excitation network-optimized temporal convolutional network, selectively focuses on informative temporal features within the electricity consumption data. The second module, a dual-dimensional attention enhanced deep residual network composed of residual blocks embedded with the convolutional block attention module, facilitates the model's concurrent learning of informative spatial and temporal features. Then, the features from each module are fused and classified through a fully connected layer. To validate the effectiveness of the proposed ETD method, this paper conducted simulation experiments using the publicly available dataset from the State Grid Corporation of China. The experimental results show that the model optimized with the attention mechanism significantly improves the performance of ETD. Compared to other ETD models, the proposed model performs excellently in various indicators under different training set ratios and sample imbalance scenarios, demonstrating good generalization and robustness. Additionally, the model was deployed on a Raspberry Pi edge computing device to further verify its feasibility in practical engineering applications.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Electricity theft detection Electricity theft detection Ensemble model Ensemble model Feature fusion Feature fusion
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| GB/T 7714 | Yang, Mingfa , Huang, Qinyu , Liu, Yulong et al. A multi-temporal granularity feature driven convolutional ensemble model for electricity theft detection [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 152 . |
| MLA | Yang, Mingfa et al. "A multi-temporal granularity feature driven convolutional ensemble model for electricity theft detection" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 152 (2025) . |
| APA | Yang, Mingfa , Huang, Qinyu , Liu, Yulong , Zheng, Xidong , Jin, Tao , Mohamed, Mohamed A. . A multi-temporal granularity feature driven convolutional ensemble model for electricity theft detection . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 152 . |
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To reduce the computation and complexity of traditional multi-vector model predictive current control (MPCC) algorithm and ensure good control performance, a novel multi-vector MPCC algorithm was proposed to quickly screen the second voltage vector. According to the principle of current non-differential beat control, the d and q-axis current decision factors were defined by the d and q-axis current difference, which simplifies the selection process of the second voltage vector, reduces the calculation of the control chip, and maintains the advantage of low current ripple. On the basis of the new two-vector MPCC algorithm, a novel three-vector MPCC algorithm combining the d and q-axis current decision factors and the effective voltage vector table to directly select the second voltage vector was proposed. Experimental results were presented showing that the new multi-vector MPCC algorithm maintains the control performance of the traditional multi-vector MPCC algorithm while the computation of the new two-vector MPCC algorithm is reduced by 33. 96% and the computation of the new three-vector MPCC algorithm is reduced by 48. 7% . © 2024 Editorial Department of Electric Machines and Control. All rights reserved.
Keyword ：
Electric current control Electric current control Permanent magnets Permanent magnets Synchronous motors Synchronous motors Vectors Vectors
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| GB/T 7714 | Yan, Chaobin , Yang, Gongde , Yang, Mingfa . New model predictive current control of permanent magnet synchronous motor [J]. | Electric Machines and Control , 2024 , 28 (5) : 91-100 . |
| MLA | Yan, Chaobin et al. "New model predictive current control of permanent magnet synchronous motor" . | Electric Machines and Control 28 . 5 (2024) : 91-100 . |
| APA | Yan, Chaobin , Yang, Gongde , Yang, Mingfa . New model predictive current control of permanent magnet synchronous motor . | Electric Machines and Control , 2024 , 28 (5) , 91-100 . |
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To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method's effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness.
Keyword ：
deep learning deep learning electricity theft detection electricity theft detection feature fusion feature fusion parallel model parallel model
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| GB/T 7714 | Huang, Qinyu , Tang, Zhenli , Weng, Xiaofeng et al. A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods [J]. | ENERGIES , 2024 , 17 (2) . |
| MLA | Huang, Qinyu et al. "A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods" . | ENERGIES 17 . 2 (2024) . |
| APA | Huang, Qinyu , Tang, Zhenli , Weng, Xiaofeng , He, Min , Liu, Fang , Yang, Mingfa et al. A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods . | ENERGIES , 2024 , 17 (2) . |
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为降低传统多矢量模型预测电流控制(MPCC)算法的计算量和复杂度,并保证良好的控制性能,提出了快速筛选第二电压矢量的新型多矢量MPCC算法.根据电流无差拍控制原则,通过直交轴电流差值定义直交轴电流判定因数,简化了第二电压矢量选择过程,减轻了控制芯片的计算量,并保持了低电流脉动的优势.在新型双矢量MPCC算法基础上,提出了结合直交轴电流判定因数和有效电压矢量表用以直接选择第二电压矢量的新型三矢量MPCC算法.实验结果表明,新型多矢量MPCC算法在保持传统多矢量MPCC算法控制性能的同时,新型双矢量MPCC算法的计算量减少33.96%,新型三矢量MPCC算法的计算量减少48.7%.
Keyword ：
三矢量 三矢量 双矢量 双矢量 模型预测电流控制 模型预测电流控制 永磁同步电机 永磁同步电机 电流判定因数 电流判定因数 计算复杂度 计算复杂度
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| GB/T 7714 | 颜朝斌 , 杨公德 , 杨明发 . 永磁同步电机新型模型预测电流控制 [J]. | 电机与控制学报 , 2024 , 28 (5) : 91-100 . |
| MLA | 颜朝斌 et al. "永磁同步电机新型模型预测电流控制" . | 电机与控制学报 28 . 5 (2024) : 91-100 . |
| APA | 颜朝斌 , 杨公德 , 杨明发 . 永磁同步电机新型模型预测电流控制 . | 电机与控制学报 , 2024 , 28 (5) , 91-100 . |
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In this article, a novel three-winding coupled inductor (TWCL) based high-gain dc-dc converter is proposed, which combines a TWCL technique and a voltage multiplier cell, avoiding large duty cycle and resulting in a high gain of the converter. The input current continuity and common ground make the proposed converter suitable for renewable energy. In addition, the converter also has the characteristics of low voltage stress on switch and high efficiency. A passive clamp circuit recycles the energy stored in the leakage inductor of the coupled inductor and reduces the voltage spike of the switch, thus improving efficiency. The steady-state analysis, parameter design, and loss analysis of the proposed converter are discussed in detail, and the comparison among existing high-gain converters is discussed as well. Finally, experimental results verify the theoretical analysis.
Keyword ：
Common ground Common ground continuous input current continuous input current dc-dc converter dc-dc converter high gain high gain three-winding coupled inductor (TWCL) three-winding coupled inductor (TWCL)
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| GB/T 7714 | Yang, Mingfa , Weng, Yusen , Li, Haibin et al. A Novel Three-Winding Coupled Inductor-Based High-Gain DCDC Converter With Low Switch Stress and Continuous Input Current [J]. | IEEE TRANSACTIONS ON POWER ELECTRONICS , 2023 , 38 (12) : 15781-15791 . |
| MLA | Yang, Mingfa et al. "A Novel Three-Winding Coupled Inductor-Based High-Gain DCDC Converter With Low Switch Stress and Continuous Input Current" . | IEEE TRANSACTIONS ON POWER ELECTRONICS 38 . 12 (2023) : 15781-15791 . |
| APA | Yang, Mingfa , Weng, Yusen , Li, Haibin , Lin, Jiaqi , Yan, Xu , Jin, Tao . A Novel Three-Winding Coupled Inductor-Based High-Gain DCDC Converter With Low Switch Stress and Continuous Input Current . | IEEE TRANSACTIONS ON POWER ELECTRONICS , 2023 , 38 (12) , 15781-15791 . |
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本文针对永磁同步电机匝间短路和失磁故障进行研究,提出了一种基于mixup数据增强和机器学习分类器的故障诊断方法.该方法提取通过小波包分解提取定子电流信号中的故障特征建立故障诊断样本,结合mixup实现样本扩张,避免小样本带来的过拟合问题.最后将扩张样本输入长短时记忆网络(long short-term memory,LSTM)进行分类.结果表明,该方法能够高效地实现永磁同步电机故障诊断,且具有较高的准确度和较强的抗噪性能.
Keyword ：
故障诊断 故障诊断 数据增强 数据增强 永磁同步电机 永磁同步电机 长短时记忆网络 长短时记忆网络
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| GB/T 7714 | 张立松 , 杨明发 . 基于mixup-LSTM的永磁同步电机故障诊断方法 [J]. | 电气开关 , 2022 , 60 (5) : 58-62 . |
| MLA | 张立松 et al. "基于mixup-LSTM的永磁同步电机故障诊断方法" . | 电气开关 60 . 5 (2022) : 58-62 . |
| APA | 张立松 , 杨明发 . 基于mixup-LSTM的永磁同步电机故障诊断方法 . | 电气开关 , 2022 , 60 (5) , 58-62 . |
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无刷直流电机的控制方便,效率突出等特点,依赖于较高的调速性能.传统调速系统所采用的PID调速,日益无法满足工业应用对精度、抗干扰,自适应等调控品质的要求.本文针对以上不足,采用基于改进型粒子群算法的PID调速系统来对电机转速进行调控.鉴于粒子群算法寻优速度不匹配,且易过早陷入局部最优等问题,采用自适应惯性权重法来进行优化改进.通过编写Matlab编程和搭建simulink模型仿真对比,可以看到相对于传统PID控制器,改进后粒子群算法能使控制器有更小的超调量、更快的响应速度和更强的抗干扰能力,显著提高无刷直流电机调速系统性能.
Keyword ：
优化PID控制器 优化PID控制器 改进型粒子群算法 改进型粒子群算法 无刷直流电机 无刷直流电机
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| GB/T 7714 | 远世明 , 杨明发 . 基于改进型粒子群算法的无刷直流电机速度控制研究 [J]. | 电气开关 , 2021 , 59 (1) : 34-38 . |
| MLA | 远世明 et al. "基于改进型粒子群算法的无刷直流电机速度控制研究" . | 电气开关 59 . 1 (2021) : 34-38 . |
| APA | 远世明 , 杨明发 . 基于改进型粒子群算法的无刷直流电机速度控制研究 . | 电气开关 , 2021 , 59 (1) , 34-38 . |
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目前,已有的高压柔性直流输电工程大多都采用包含半桥子模块的模块化多电平换流器(MMC),但是半桥型MMC缺乏直流故障清除能力。为解决这一问题本文在原来模块化嵌入式多电平换流器(MEMC)拓扑的基础上提出了改进型MEMC拓扑,该拓扑具有更高的可靠性和功率处理能力,并提出了一种控制技术,用全桥子模块产生的负电压对功率因数滞后的负载进行晶闸管的强制换相。改进型MEMC除具有直流故障清除能力外,还提供了更宽的工作范围和更小的子模块电容器尺寸。最后在PSCAD/EMTDC中搭建改进型MEMC-HVDC模型,并进行直流故障仿真,仿真结果验证了该拓扑的适用性和直流故障清除能力。
Keyword ：
改进型模块化嵌入式多电平换流器 改进型模块化嵌入式多电平换流器 晶闸管换相 晶闸管换相 模块化多电平换流器 模块化多电平换流器 直流故障清除 直流故障清除
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| GB/T 7714 | 王婷 , 杨明发 . 基于改进型模块化嵌入式多电平换流器拓扑的HVDC直流故障清除策略 [J]. | 电气技术 , 2020 , 21 (12) : 17-22,35 . |
| MLA | 王婷 et al. "基于改进型模块化嵌入式多电平换流器拓扑的HVDC直流故障清除策略" . | 电气技术 21 . 12 (2020) : 17-22,35 . |
| APA | 王婷 , 杨明发 . 基于改进型模块化嵌入式多电平换流器拓扑的HVDC直流故障清除策略 . | 电气技术 , 2020 , 21 (12) , 17-22,35 . |
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