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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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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)
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| 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 等. "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 . |
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继电器触头分断过程中产生的电弧是造成触头电烧蚀进而导致继电器失效的主要原因之一.为了方便分析交流电弧物理特性,通过对Fluent进行二次开发,基于流体动力学方程、电磁场方程建立三维交流电弧的磁流体动力学(MHD)模型.结合触头的实际运动过程,计算分析交流继电器分断电弧的动态过程.通过与实测数据进行对比,验证了电弧仿真的准确性,对进一步了解交流电弧特性及优化产品设计有一定的指导意义.
Keyword ：
Fluent Fluent 交流电磁继电器 交流电磁继电器 动态电弧 动态电弧 磁流体动力学模型 磁流体动力学模型
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| GB/T 7714 | 谢雅霜 , 鲍光海 . 交流继电器电弧动态特性仿真分析 [J]. | 电器与能效管理技术 , 2023 , (4) : 28-33 . |
| MLA | 谢雅霜 等. "交流继电器电弧动态特性仿真分析" . | 电器与能效管理技术 4 (2023) : 28-33 . |
| APA | 谢雅霜 , 鲍光海 . 交流继电器电弧动态特性仿真分析 . | 电器与能效管理技术 , 2023 , (4) , 28-33 . |
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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 等. "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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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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针对电缆接头绝缘故障难以检测的问题,设计一种基于高频电流法的电缆接头局部放电在线检测系统.系统以DSP+FPGA为核心,通过FPGA采集安装在电缆接头接地线上高频电流互感器(HFCT)耦合的局部放电信号,利用DSP芯片,编写FFT和小波变换程序对局部放电信号中窄带干扰和白噪声分别进行噪声抑制,实现对电缆接头局部放电信号的在线检测.最后,将示波器采集信号与MATLAB降噪处理结果分别与在线检测系统进行比较,结果具有一致性,证明了所设计系统的有效性.
Keyword ：
DSP DSP FPGA FPGA 局部放电 局部放电 电缆接头 电缆接头 高频电流法 高频电流法
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| GB/T 7714 | 徐振 , 鲍光海 . 电缆接头局部放电在线检测系统的设计 [J]. | 电器与能效管理技术 , 2022 , (8) : 51-56 . |
| MLA | 徐振 et al. "电缆接头局部放电在线检测系统的设计" . | 电器与能效管理技术 8 (2022) : 51-56 . |
| APA | 徐振 , 鲍光海 . 电缆接头局部放电在线检测系统的设计 . | 电器与能效管理技术 , 2022 , (8) , 51-56 . |
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电缆接头温度过高引起的电缆故障是电缆输电网络的主要故障之一,为提高电缆输电的可靠性,文中将一种无源无线温度传感器埋入电缆接头中,实现对电缆接头温度的直接测量.监控主机通过超高频电磁波向温度传感器传输能量并获取电缆接头温度,同时通过NB-IoT网络将信息传送到服务器,维护人员可以通过手机AP P实时查看电缆接头的温度信息和报警信息,监控主机的电源由安装在电缆上的CT提供.文中搭建的温度在线监测系统能够实时、安全、有效监测电缆接头温度,降低事故率,为电网安全可靠运行提供技术支持.
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| GB/T 7714 | 邓志飞 , 鲍光海 . 基于超高频RFID技术的电缆接头温度在线监测系统 [J]. | 仪表技术与传感器 , 2021 , (7) : 71-75,96 . |
| MLA | 邓志飞 et al. "基于超高频RFID技术的电缆接头温度在线监测系统" . | 仪表技术与传感器 7 (2021) : 71-75,96 . |
| APA | 邓志飞 , 鲍光海 . 基于超高频RFID技术的电缆接头温度在线监测系统 . | 仪表技术与传感器 , 2021 , (7) , 71-75,96 . |
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This article presents a new method for effective detection of ac series arc fault (AF) (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and nonarcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain highfrequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the nonarcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to the UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting AFs compared with existing methods.
Keyword ：
Coupling signals Coupling signals series arc fault (SAF) series arc fault (SAF) short-observation-window singular value decomposition and reconstruction (SOW-SVDR) short-observation-window singular value decomposition and reconstruction (SOW-SVDR) UL1699 UL1699
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| GB/T 7714 | Jiang, Run , Bao, Guanghai , Hong, Qiteng et al. A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
| MLA | Jiang, Run et al. "A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70 (2021) . |
| APA | Jiang, Run , Bao, Guanghai , Hong, Qiteng , Booth, Campbell D. . A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2021 , 70 . |
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