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学者姓名：鲍光海
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Abstract ：
AC series arc faults (SAFs) are one of the leading causes of electrical fires in buildings, and the development of arc fault detection devices (AFDDs) can effectively reduce the fire risk caused by arc faults. To address the issue of unsatisfactory detection performance for SAFs and frequent false positives in existing AFDDs when dealing with unknown load combinations, this paper proposes an adaptive SAF detection system. The system is based on the remote interaction between AFDD and cloud server, which enables the AFDD to update its SAF detection model for unknown load combinations, thereby improving its generalization performance. First, a lightweight neural network model for SAF detection based on depth-wise separable convolution and inverted residual block was designed and ported to the K210 chip, combined with peripheral circuits to create the AFDD. The AFDD collects high-frequency coupling signals from the circuit at a sampling rate of 100 kHz, achieving real-time SAF detection with a detection cycle of 80 ms. The cloud server receives and filters false positive and SAF data uploaded by the AFDD during operation, and updates the detection model on the AFDD through data augmentation and transfer learning to improve its generalization capability. Experimental results show that the normal state recognition rate of the updated AFDD for unknown load combinations increased from 98.87% to 99.92%, and the SAF recognition rate improved from 96.26% to 98.16%. The results demonstrate that the adaptive SAF detection system significantly improves the AFDD's performance in reducing false positives and missed detections for unknown load combinations.
Keyword ：
AC series arc faults AC series arc faults arc fault detection device arc fault detection device cloud-edge collaboration cloud-edge collaboration deep learning deep learning transfer learning transfer learning
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| GB/T 7714 | Bao, Guanghai , Wang, Zhaorui , He, Jiantao . Research on a cloud-edge collaborative adaptive detection system for AC series arc faults [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (2) . |
| MLA | Bao, Guanghai 等. "Research on a cloud-edge collaborative adaptive detection system for AC series arc faults" . | MEASUREMENT SCIENCE AND TECHNOLOGY 36 . 2 (2025) . |
| APA | Bao, Guanghai , Wang, Zhaorui , He, Jiantao . Research on a cloud-edge collaborative adaptive detection system for AC series arc faults . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (2) . |
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Abstract ：
为杜绝安全隐患,利用Ⅴ-Ⅰ轨迹和改进MobileNetv2模型对入户充电行为进行在线辨识.设计实验场景,从采样率选取、迁移学习、泛化性和不同网络对比4个方面验证模型性能,最后把模型部署到上位机和K210芯片上.上位机系统在电动自行车单独充电时准确识别,当充电行为和常用家庭负载混合运行时,识别准确率达到98％以上.
Keyword ：
在线识别 在线识别 改进MobileNetv2模型 改进MobileNetv2模型 Ⅴ-Ⅰ轨迹 Ⅴ-Ⅰ轨迹 迁移学习 迁移学习
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| GB/T 7714 | 段佳其 , 鲍光海 , 方艳东 . 基于Ⅴ-Ⅰ轨迹的非侵入式电动自行车充电行为在线辨识 [J]. | 电器与能效管理技术 , 2024 , (12) : 69-76 . |
| MLA | 段佳其 等. "基于Ⅴ-Ⅰ轨迹的非侵入式电动自行车充电行为在线辨识" . | 电器与能效管理技术 12 (2024) : 69-76 . |
| APA | 段佳其 , 鲍光海 , 方艳东 . 基于Ⅴ-Ⅰ轨迹的非侵入式电动自行车充电行为在线辨识 . | 电器与能效管理技术 , 2024 , (12) , 69-76 . |
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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
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| 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 . |
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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
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| 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 et al. "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 . |
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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
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| 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 et al. "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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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