Query：
学者姓名：鲍光海
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
针对非线性负载和复杂组合负载发生串联电弧故障时检测效果不理想，提出一种基于改进轻量型网络MobileNet的串联电弧故障检测方法。根据磁通不对称分布将中性线和相线同时穿过电流互感器获取高频电流耦合信号，通过在实验室模拟大量电弧故障实验，获得国标规定7种负载与常见家用电器5种负载的各类电弧故障样本，运用数据增强生成数据集进行训练并测试，最后将串联电弧故障检测网络模型搭载于电弧故障保护样机上，进行在线检测测试。实验结果表明，所提方法能有效识别串联电弧故障，且有利于实现电弧故障保护器的产品化。
Keyword ：
不对称分布 不对称分布 串联电弧故障 串联电弧故障 故障检测 故障检测 轻量型网络 轻量型网络
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 郑力 , 鲍光海 . 基于改进MobileNet的串联电弧故障检测方法 [J]. | 电器与能效管理技术 , 2024 , 8 (02) : 13-20 . |
| MLA | 郑力 等. "基于改进MobileNet的串联电弧故障检测方法" . | 电器与能效管理技术 8 . 02 (2024) : 13-20 . |
| APA | 郑力 , 鲍光海 . 基于改进MobileNet的串联电弧故障检测方法 . | 电器与能效管理技术 , 2024 , 8 (02) , 13-20 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
窃电行为不仅会造成电网非技术性损耗增加，而且可能因操作不当影响电网设备的运行安全和窃电者的人身安全。针对当前电网在窃电检测方面存在的稽查难度大、检测效率低等问题，设计了窃电监测系统。配套监测装置可灵活安装在供电线路上，使用电流互感器取能，实时采集线路电流，利用4G模块将数据传输至云服务器，在上位机软件中采用实值深度置信网络(RDBN)算法对数据进行分析。仿真和实验测试表明，RDBN算法对窃电状态的识别准确率达到98.15%,监测系统能实时获取并分析监测数据，标记可疑窃电线路，降低稽查难度，提高检测效率。
Keyword ：
实值深度置信网络 实值深度置信网络 电流互感器取能 电流互感器取能 窃电检测 窃电检测 非技术性损耗 非技术性损耗
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 陈志谦 , 鲍光海 , 方艳东 . 基于RDBN深度学习算法的窃电监测系统设计 [J]. | 电器与能效管理技术 , 2024 , PageCount-页数: 8 (05) : 67-74 . |
| MLA | 陈志谦 等. "基于RDBN深度学习算法的窃电监测系统设计" . | 电器与能效管理技术 PageCount-页数: 8 . 05 (2024) : 67-74 . |
| APA | 陈志谦 , 鲍光海 , 方艳东 . 基于RDBN深度学习算法的窃电监测系统设计 . | 电器与能效管理技术 , 2024 , PageCount-页数: 8 (05) , 67-74 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为保证光伏发电系统的安全稳定运行，提出一种基于轻量型卷积神经网络(CNN)和特征阈值的光伏串联电弧故障检测算法。为了应对逆变器异常工况和光伏阵列时变性对信号特征的影响，以及不同弧长(0.05～10.00 mm)导致的信号特征差异，利用高频耦合信号为特征信号，并结合神经网络算法和特征阈值方法，检测光伏线路上的串联电弧故障。最后，制作光伏串联电弧故障检测装置样机。经实验测试，样机切断电弧故障的平均时间为177.1 ms,且在逆变器异常工况的干扰下不会发生误判。
Keyword ：
串联电弧故障 串联电弧故障 光伏系统 光伏系统 卷积神经网络 卷积神经网络 电弧检测装置 电弧检测装置
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 王兆锐 , 何键涛 , 李治彤 et al. 基于轻量型CNN和特征阈值的光伏系统串联电弧故障检测装置 [J]. | 电器与能效管理技术 , 2024 , PageCount-页数: 9 (08) : 77-85 . |
| MLA | 王兆锐 et al. "基于轻量型CNN和特征阈值的光伏系统串联电弧故障检测装置" . | 电器与能效管理技术 PageCount-页数: 9 . 08 (2024) : 77-85 . |
| APA | 王兆锐 , 何键涛 , 李治彤 , 鲍光海 . 基于轻量型CNN和特征阈值的光伏系统串联电弧故障检测装置 . | 电器与能效管理技术 , 2024 , PageCount-页数: 9 (08) , 77-85 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In order to speed up the construction of energy saving and emission reduction, and strengthen the power consumption management on the demand side, the non-intrusive load monitoring has become a research hotspot because of its easy implementation and reliability. However, the current research has some problems, such as low identification accuracy of low-frequency data load, complex extraction of high-frequency data features and poor network generalization performance. Therefore, this paper proposes a non-intrusive load monitoring based on ResNeXt network and transfer learning. The one-dimensional time-series total power is converted into two-dimensional image with time characteristics as input through Gram angle field (GAF) algorithm, and the image is put into ResNeXt network under transfer learning for load identification. This method uses the low-frequency data that can be collected by the existing meter as the input, reduces the data input dimension and adds time characteristics. And then, after the images are standardized, the deep load information is learned by stacking the residual neural network with deep-layers, and the trained network model parameters under ImageNet-1K dataset are transferred to the new target domain by transfer learning, so as to accelerate the convergence speed of the network, improve the accuracy of load classification and the generalization of the network. Finally, this method is verified by using the open data sets AMPds and UK-DALE to simulate different power consumption scenarios, and the accuracy is above 99%, which verifies the efficiency and generalization of the proposed method. © 2023 Automation of Electric Power Systems Press. All rights reserved.
Keyword ：
Gram angle field (GAF) algorithm Gram angle field (GAF) algorithm image encoding image encoding non-intrusive load monitoring non-intrusive load monitoring residual neural network residual neural network transfer learning transfer learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Bao, G. , Huang, Y. . Non-intrusive Load Monitoring Based on ResNeXt Network and Transfer Learning; [基于 ResNeXt 网络和迁移学习的非侵入式负荷监测] [J]. | Automation of Electric Power Systems , 2023 , 47 (13) : 110-120 . |
| MLA | Bao, G. et al. "Non-intrusive Load Monitoring Based on ResNeXt Network and Transfer Learning; [基于 ResNeXt 网络和迁移学习的非侵入式负荷监测]" . | Automation of Electric Power Systems 47 . 13 (2023) : 110-120 . |
| APA | Bao, G. , Huang, Y. . Non-intrusive Load Monitoring Based on ResNeXt Network and Transfer Learning; [基于 ResNeXt 网络和迁移学习的非侵入式负荷监测] . | Automation of Electric Power Systems , 2023 , 47 (13) , 110-120 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Contact wear and contact adhesion caused by contact bounce have an important impact on the electrical life of relay. Considering the complex contact bounce of the magnetic latching relay under the interaction of elastic deformation of the reed and contact collision, the processing method defining the model as rigid body cannot restore the actual motion state of the flexible reed. The collision of the rigid body leads to the deformation of the spring of the flexible body, which affects the action time and bounce of the relay. The accurate simulation of the movement state of the reed is an important prerequisite to ensure that the simulation results of dynamic characteristics have reliable reference value. In order to improve the accuracy and reliability of dynamic characteristics simulation, a rigid flexible coupling dynamic model is established to simulate the movement state of the relay. The traditional rigid flexible coupling method of model is to obtain the modal neutral file required for the establishment of flexible body with the aid of finite element software, and realize the coupling of rigid flexible model by importing the modal neutral file into the rigid body model of multi-body dynamics software. In addition to material properties, the deformation of flexible bodies is also affected by the mode order and the key points derived from the modal neutral file. Based on the motion equation, material constitutive equation and boundary conditions, the mathematical model of the motion field of the relay is established. Considering the impact of collision deformation of components on relay operation process, symmetrical penalty function method, hourglass coefficient and damping coefficient are used to deal with collision, contact and deformation of components. With the dynamics software Ansys LS-DYNA, the rigid flexible coupling model of relay electromagnetic system, contact system and transmission mechanism is established. Use APDL to build mechanical dynamics model. The electromagnetic model of relay is established based on Maxwell equations, the three-dimensional finite element model of electromagnetic system components is established by using the electromagnetic software Ansys Maxwell, and the external circuit of relay excitation coil is established by using the simulation software Ansys Simplorer. The electromagnetic system component model and the external circuit of the coil are coupled to form the relay electromagnetic model. Compared with the traditional circuit editor coupling mode, Ansys Simplorer has a stronger control function and a Simulink communication interface to receive the motion data of the relay. Simulink is used to control the start stop and data processing of the electromagnetic model. There is no ready-made data interaction channel between the electromagnetic model and the dynamic model. The Matlab program is used to control the data exchange of different physical field models, so as to realize the coupling calculation and data interaction of multiple physical fields of the two models in the same time domain. The simulation results of the relay coil current curve, the reed shape variable of the dynamic model and the contact movement track are basically consistent with the measured results; Both the simulation and actual measurement of contact bounce show the phenomenon of bounce pull in bounce stable pull in, which verifies the accuracy of the multi physical field coupling model. On the basis of simulation, the influence of static spring elastic modulus, core coil voltage and normally closed static spring preload on contact bounce is investigated. The 3D transient multi physical field coupling simulation can truly restore the working state of the relay product, simulate the dynamic characteristics of the relay and the contact movement. The larger elastic modulus and the larger pre pressure of the normally closed static spring can optimize the structural design of the relay and inhibit the contact bounce. © 2023 Chinese Machine Press. All rights reserved.
Keyword ：
Ansys LS-DYNA Ansys LS-DYNA contact bounce contact bounce dynamic characteristics dynamic characteristics Magnetic latching relay Magnetic latching relay
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Bao, G. , Wang, J. , Wang, Y. . Design of Multi-Physical Field Coupling Model of Magnetic Latching Relay and Analysis of Influencing Factors of Contact Bounce; [磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析] [J]. | Transactions of China Electrotechnical Society , 2023 , 38 (3) : 828-840 . |
| MLA | Bao, G. et al. "Design of Multi-Physical Field Coupling Model of Magnetic Latching Relay and Analysis of Influencing Factors of Contact Bounce; [磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析]" . | Transactions of China Electrotechnical Society 38 . 3 (2023) : 828-840 . |
| APA | Bao, G. , Wang, J. , Wang, Y. . Design of Multi-Physical Field Coupling Model of Magnetic Latching Relay and Analysis of Influencing Factors of Contact Bounce; [磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析] . | Transactions of China Electrotechnical Society , 2023 , 38 (3) , 828-840 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
触头弹跳引起的触点磨损和触点粘接等故障对继电器电寿命有重要影响.考虑磁保持继电器在簧片弹性形变和触头碰撞的相互作用下触头复杂的弹跳情况,将模型定义为刚体的处理方法不能还原柔性体簧片的实际运动状态.通过Ansys LS-DYNA,基于运动方程、材料本构方程和边界条件建立继电器动力学数学模型.通过Ansys Maxwell,基于麦克斯韦方程组建立继电器电磁学模型.通过Matlab交换不同物理场模型数据,实现相同时间域内两种模型多物理场的耦合计算和数据交互.通过与实验数据对比验证了仿真模型的准确性.在仿真基础上,探究静簧片弹性模量、铁心线圈电压和常闭静簧片预压力对触头弹跳的影响.证明了通过三维瞬态多物理场耦合仿真能够真实地还原继电器产品的工作状态,缩短产品的开发设计周期.
Keyword ：
Ansys LS-DYNA Ansys LS-DYNA 动态特性 动态特性 磁保持继电器 磁保持继电器 触头弹跳 触头弹跳
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 鲍光海 , 王金鹏 , 王毅龙 . 磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析 [J]. | 电工技术学报 , 2023 , 38 (3) : 828-840 . |
| MLA | 鲍光海 et al. "磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析" . | 电工技术学报 38 . 3 (2023) : 828-840 . |
| APA | 鲍光海 , 王金鹏 , 王毅龙 . 磁保持继电器多物理场耦合模型设计与触头弹跳影响因素分析 . | 电工技术学报 , 2023 , 38 (3) , 828-840 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Ac series arc fault detection under various circuits is always a challenging task because arcing current is affected by different circuit types and fault points. Additionally, normal current is confused as arcing current when nonlinear loads are present in the circuit. To cope with these issues, this article presents a detection method using signal-type enumeration and a zoom circular convolution (CC) (ZCC) algorithm. The generalization ability of the detection method under unknown conditions is improved by means of signal-type enumeration. The impulsive components of current can be divided into stable, periodically impulsive, nonperiodically impulsive, and hybrid signals by enumeration. Then, given the CC limitations, the ZCC is presented to extract distinguishable features and decrease the computation complexity of CC, and the signal function is partially reconstructed to improve the CC performance. Finally, the presented detection method is analyzed in a laptop and evaluated by TMS320F28335. The online detection results show that the proposed method determined by known single-load circuits has good detection accuracy under unknown conditions and can be achieved in practical application.
Keyword ：
Ac series arc fault (SAF) detection Ac series arc fault (SAF) detection Circuit faults Circuit faults Feature extraction Feature extraction impulsive components impulsive components Integrated circuit modeling Integrated circuit modeling Load modeling Load modeling Microprocessors Microprocessors Resistance Resistance signal-type enumeration signal-type enumeration Time-domain analysis Time-domain analysis various circuits various circuits zoom circular convolution (ZCC) zoom circular convolution (ZCC)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jiang, Run , Bao, Guanghai . Series Arc Fault Detection Method Based on Signal-Type Enumeration and Zoom Circular Convolution Algorithm [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 , 70 (10) : 10607-10617 . |
| MLA | Jiang, Run et al. "Series Arc Fault Detection Method Based on Signal-Type Enumeration and Zoom Circular Convolution Algorithm" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 70 . 10 (2023) : 10607-10617 . |
| APA | Jiang, Run , Bao, Guanghai . Series Arc Fault Detection Method Based on Signal-Type Enumeration and Zoom Circular Convolution Algorithm . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 , 70 (10) , 10607-10617 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
During ac series arc faults (SAFs), arcing current features can change significantly or vanish rapidly under different load-combination modes and fault inception points. The phenomena make it very challenging for feature-extracting algorithms to detect SAFs. To address the issues, this article presents a detection model based on regular coupling features (RCFs). After the model is only trained by the samples in single-load circuits, it can detect SAFs under unknown multiload circuits. To extract the RCFs, asymmetric magnetic flux is coupled by passing the live line and the neutral line through the current transformer. The coupling signals are not influenced by the multiload circuits. According to the unique signals, two time-domain features and one frequency-domain feature are extracted to represent the RCFs, including impulse-factor analysis, covariance-matrix analysis, and multiple frequency-band analysis. Then, the impulse factor and its threshold are used to preprocess the signals and decrease analysis complexity for the classifier. Finally, the experimental results show that the proposed method has significantly improved generalization ability and detection accuracy in SAF detection.
Keyword ：
AC series arc faults (SAF) AC series arc faults (SAF) Classification algorithms Classification algorithms Couplings Couplings covariance matrix analysis (CMA) covariance matrix analysis (CMA) Feature extraction Feature extraction generalization ability generalization ability impulse-factor analysis (IFA) impulse-factor analysis (IFA) Informatics Informatics multiple frequency-band analysis (MFA) multiple frequency-band analysis (MFA) regular coupling feature (RCF) regular coupling feature (RCF) Resistance Resistance Time-domain analysis Time-domain analysis Time-frequency analysis Time-frequency analysis
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jiang, Run , Bao, Guanghai , Hong, Qiteng et al. Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2023 , 19 (3) : 2761-2771 . |
| MLA | Jiang, Run et al. "Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19 . 3 (2023) : 2761-2771 . |
| APA | Jiang, Run , Bao, Guanghai , Hong, Qiteng , Booth, Campbell D. . Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2023 , 19 (3) , 2761-2771 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为了加快节能减排的建设和加强需求侧的用电管理,非侵入式负荷监测凭借其易实施性和可靠性等特点已成为研究热点,但目前的研究存在着低频数据负荷识别精度低、高频数据特征提取复杂及网络泛化性能差等问题.因此,提出基于ResNeXt网络和迁移学习的非侵入式负荷监测,采用一维时间序列总功率通过格拉姆角场(GAF)算法转换为带有时间特性的二维图像作为输入,放入迁移学习下ResNeXt网络进行负荷识别.该方法采用现有电表采集的低频数据作为输入,减少数据输入维度并加入了时间特性,再将输入图像进行标准化处理后通过堆叠深层次的残差神经网络来学习负荷深层次信息,利用迁移学习将在ImageNet-1K数据集下已训练好的网络模型参数传入新的目标域,加快网络的收敛速度,提高负荷分类的识别准确率和网络的泛化性.最后,利用公开数据集AMPds和UK-DALE模拟不同用电场景验证了所提方法的高效性和泛化性.
Keyword ：
图像编码 图像编码 格拉姆角场算法 格拉姆角场算法 残差神经网络 残差神经网络 迁移学习 迁移学习 非侵入式负荷监测 非侵入式负荷监测
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 鲍光海 , 黄逸欣 . 基于ResNeXt网络和迁移学习的非侵入式负荷监测 [J]. | 电力系统自动化 , 2023 , 47 (13) : 110-120 . |
| MLA | 鲍光海 et al. "基于ResNeXt网络和迁移学习的非侵入式负荷监测" . | 电力系统自动化 47 . 13 (2023) : 110-120 . |
| APA | 鲍光海 , 黄逸欣 . 基于ResNeXt网络和迁移学习的非侵入式负荷监测 . | 电力系统自动化 , 2023 , 47 (13) , 110-120 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
AC series arc faults in the power system can lead to electrical fires. However, the generalization performance of the determined detection method would be affected under unknown loads, as current features vary with loads. To address this issue, this article presents a series arc fault detection method based on a high-frequency (HF) RLC arc model and 1-D convolutional neural network (1DCNN). By the current transformer used for receiving differential HF features (D-HFCT), current with complex features is first simplified and divided into different oscillation signal types. Since the types of real D-HFCT data are limited, the RLC arc model is used to generate D-HFCT data with various types of oscillation features by adjusting load types, initial phase angles, and Bernoulli-sequence frequencies. Then, the simulated data are adopted to train the 1DCNN model. Finally, the trained 1DCNN model can detect series arc faults under different types of real loads. Compared with the 1DCNN method driven by the limited types of real-current data, the presented method shows good generalization ability and achieves 99.33% average detection accuracy under nine types of unknown loads, which benefits from the training of simulated D-HFCT data with abundant HF oscillation features. [GRAPHICS] .
Keyword ：
1-D convolutional neural network (1DCNN) 1-D convolutional neural network (1DCNN) ac series arc faults ac series arc faults fault detection fault detection high-frequency (HF) oscillation features high-frequency (HF) oscillation features RLC-based arc model RLC-based arc model
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jiang, Run , Wang, Yilong , Gao, Xiaoqing et al. AC Series Arc Fault Detection Based on RLC Arc Model and Convolutional Neural Network [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (13) : 14618-14627 . |
| MLA | Jiang, Run et al. "AC Series Arc Fault Detection Based on RLC Arc Model and Convolutional Neural Network" . | IEEE SENSORS JOURNAL 23 . 13 (2023) : 14618-14627 . |
| APA | Jiang, Run , Wang, Yilong , Gao, Xiaoqing , Bao, Guanghai , Hong, Qiteng , Booth, Campbell D. . AC Series Arc Fault Detection Based on RLC Arc Model and Convolutional Neural Network . | IEEE SENSORS JOURNAL , 2023 , 23 (13) , 14618-14627 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
