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学者姓名：杨耿杰
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Abstract ：
针对现有配电网弧光高阻接地故障波形多样、仿真所得波形与工程实际差异较大,导致已有故障识别模型难以泛化等问题,本文首先分析已有的仿真模型伏安特性,然后通过试验获取实测伏安特性,并分析仿真波形与实测波形的差异性,最后对影响波形的故障非线性、随机性、间歇性、热惯性、零休偏移等特性进行总结,提出在考虑无功分量的基础上改进建模方法的建议.
Keyword ：
仿真模型 仿真模型 伏安特性 伏安特性 差异性 差异性 弧光高阻接地故障 弧光高阻接地故障 配电网 配电网
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| GB/T 7714 | 林万里 , 杨耿杰 , 郭谋发 . 配电网弧光高阻接地故障仿真与实测波形差异性分析 [J]. | 电气技术 , 2025 , 26 (6) : 38-44 . |
| MLA | 林万里 等. "配电网弧光高阻接地故障仿真与实测波形差异性分析" . | 电气技术 26 . 6 (2025) : 38-44 . |
| APA | 林万里 , 杨耿杰 , 郭谋发 . 配电网弧光高阻接地故障仿真与实测波形差异性分析 . | 电气技术 , 2025 , 26 (6) , 38-44 . |
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考虑到传统的基于电磁辐射(electromagnetic radiation,EMR)信号的光伏阵列电弧故障定位方法存在采样条件严苛、定位精度低等问题,提出一种基于网格指纹匹配的电弧故障定位新方法.首先,使用低采样率获取电弧EMR信号,并提取其均方根值作为代表EMR强度的特征指标.然后,利用BP神经网络(back propagation neural network,BPNN)挖掘辐照度、信号接收距离与电弧EMR信号强度的内在联系,建立预测模型.接着,根据BPNN输出的双天线阵列与电弧间的预测距离,利用三角定位法初步求得电弧所在区域.最后,网格化划分电弧所在区域的光伏组件,生成网格指纹信息,并将预测距离与指纹信息最匹配的网格的中心坐标作为电弧发生位置的最终预测坐标.实验结果表明,所提算法具备良好的定位能力与适应性,对电弧故障定位的平均绝对误差为0.306 m,在定位精度与经济性上均优于EMR衰减模型定位法.
Keyword ：
BP神经网络 BP神经网络 光伏阵列 光伏阵列 电弧故障定位 电弧故障定位 电磁辐射 电磁辐射 网格指纹匹配 网格指纹匹配
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| GB/T 7714 | 金辉 , 高伟 , 林亮世 et al. 基于网格指纹匹配的光伏阵列电弧故障定位方法 [J]. | 高电压技术 , 2024 , 50 (2) : 834-845 . |
| MLA | 金辉 et al. "基于网格指纹匹配的光伏阵列电弧故障定位方法" . | 高电压技术 50 . 2 (2024) : 834-845 . |
| APA | 金辉 , 高伟 , 林亮世 , 杨耿杰 . 基于网格指纹匹配的光伏阵列电弧故障定位方法 . | 高电压技术 , 2024 , 50 (2) , 834-845 . |
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The complexity and uncertainty of vibration signals from distribution transformers pose significant challenges for diagnosing mechanical faults. To address this, this paper proposes a novel fault diagnosis model for distribution transformers, which combines a cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) with an open-set domain adaptation classifier (OSDA-C). Specifically, in order to extract more comprehensive features, a convolutional autoencoder (CAE) model based on multi-output objectives is constructed to extract the timefrequency domain characteristics of transformer vibration signals. Multiple-scale convolutional layers are incorporated into the convolutional autoencoder to enable multi-range feature extraction. Additionally, parameter optimization is achieved using the crayfish optimization algorithm (COA). Subsequently, an open-set domain adaptation module is integrated into the convolutional neural network classifier to establish boundaries for each category and facilitate the identification of transformer fault categories, including unknown-type faults. The experimental results demonstrate that the proposed method is effective for fault identification in both drytype and oil-immersed transformers, with average accuracy reaching 99.35% and 99.62%, respectively. For unknown-type faults, the accuracy also achieved 100% and 97.5%, respectively.
Keyword ：
Cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) Cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) Distribution transformer Distribution transformer Mechanical faults Mechanical faults Open-set domain adaptation classifier(OSDA-C) Open-set domain adaptation classifier(OSDA-C) Vibration signals Vibration signals
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| GB/T 7714 | Huang, Haiyan , Gao, Wei , Yang, Gengjie . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults [J]. | MEASUREMENT , 2024 , 238 . |
| MLA | Huang, Haiyan et al. "Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults" . | MEASUREMENT 238 (2024) . |
| APA | Huang, Haiyan , Gao, Wei , Yang, Gengjie . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults . | MEASUREMENT , 2024 , 238 . |
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The problems of strict sampling conditions and low positioning accuracy exist in the traditional photovoltaic array arc fault location method based on electromagnetic radiation (EMR) signal, accordingly, we propose a new arc fault location method based on grid fingerprint matching. Firstly, the EMR signal of the arc is acquired with a low sampling rate, and its root mean square value is extracted as the characteristic index representing the EMR intensity. Then, BP neural network (BPNN) is adopted to mine the internal relationship among irradiance, signal receiving distance and arc EMR signal intensity, and a prediction model is established. Subsequently, according to the predicted distance between the dual-antenna array output by BPNN and the arc, the area where the arc is located is preliminarily acquired by using the triangulation method. Finally, the photovoltaic module in the located area is divided into grids to generate grid fingerprint information, and the center coordinate of the grid that most matches the predicted distance and fingerprint information is taken as the final predicted coordinate of the arc occurrence position. The experiment results show that the proposed algorithm has good positioning ability and adaptability, and the average absolute error of arc fault location is 0.306 m, which is superior to the EMR attenuation model positioning method in positioning accuracy and economy. © 2024 Science Press. All rights reserved.
Keyword ：
Antenna arrays Antenna arrays Backpropagation Backpropagation Electromagnetic wave emission Electromagnetic wave emission Location Location Neural networks Neural networks Pattern matching Pattern matching
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| GB/T 7714 | Jin, Hui , Gao, Wei , Lin, Liangshi et al. Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching [J]. | High Voltage Engineering , 2024 , 50 (2) : 805-815 . |
| MLA | Jin, Hui et al. "Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching" . | High Voltage Engineering 50 . 2 (2024) : 805-815 . |
| APA | Jin, Hui , Gao, Wei , Lin, Liangshi , Yang, Gengjie . Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching . | High Voltage Engineering , 2024 , 50 (2) , 805-815 . |
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DC arc faults are major causes of electrical fires in photovoltaic (PV) systems. During the operation and maintenance of these systems, it is essential not only to identify arc faults but also to determine their exact locations accurately. To address the issue of DC arc fault localization in PV systems, this study investigates the electromagnetic radiation (EMR) characteristics of fault arcs and proposes a method for DC arc fault localization using the redundant antenna array and the ellipse algorithm. Firstly, during arc combustion, the EMR signals collected by antennas are subjected to median filtering to calculate the root mean square (RMS), which serves as the signal strength. An artificial neural network (ANN) model is constructed, which uses the signal strength and irradiance to predict the distance between the fault point and the receiving point. Subsequently, various redundant antenna array configurations are evaluated to assess the impact of different antenna quantities and layouts on localization accuracy. Once the optimal layout is determined, the three antennas with the strongest signal are selected. Their coordinates, along with the predicted distances to the fault point, are input into the ellipse algorithm, which is improved by trilateration, to obtain the locations of arc faults. Finally, the density-based spatial clustering of applications with noise (DBSCAN) method is used to fuse multiple measurement results, eliminate interference, and confirm the final fault coordinates. Experimental results demonstrate that the proposed location method exhibits excellent positioning capability and adaptability, with an average positioning error of 0.365 m.
Keyword ：
Arc fault location Arc fault location DBSCAN DBSCAN Ellipse algorithm Ellipse algorithm Photovoltaic systems Photovoltaic systems Redundant antenna array Redundant antenna array
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| GB/T 7714 | Lin, Liangshi , Gao, Wei , Yang, Gengjie . A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm [J]. | SOLAR ENERGY , 2024 , 274 . |
| MLA | Lin, Liangshi et al. "A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm" . | SOLAR ENERGY 274 (2024) . |
| APA | Lin, Liangshi , Gao, Wei , Yang, Gengjie . A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm . | SOLAR ENERGY , 2024 , 274 . |
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近年来,新能源和电动汽车的渗透比例逐渐增高,给配电网的潮流优化和电能质量治理带来严峻挑战.针对分布式电源的随机性和间歇性问题,设计一种基于递归径向基神经网络(RRBFNN)滑模的多功能柔性多状态开关(FMS)控制方法,在实现功率交互和多端单相接地故障柔性消弧的同时,增强 FMS 的抗扰能力.首先考虑扰动的影响,设计一种改进 RRBFNN 滑模控制方法,以克服传统滑模控制固有的抖振现象和对系统精确数学模型的依赖,并减小并网暂态冲击;柔性消弧控制采用微积分型滑模面,理论推导出 0 轴电压控制律,提高故障电流抑制率;进一步通过李雅普诺夫定理证明所设计方法的稳定性和收敛性.最后,在Matlab/Simulink中搭建三端口FMS及其控制系统的仿真模型,通过对比仿真验证了所提策略的可行性和有效性.
Keyword ：
单相接地故障 单相接地故障 径向基神经网络(RBFNN) 径向基神经网络(RBFNN) 柔性多状态开关(FMS) 柔性多状态开关(FMS) 柔性消弧 柔性消弧 滑模控制 滑模控制 配电网 配电网
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| GB/T 7714 | 廖江华 , 高伟 , 唐钧益 et al. 基于递归径向基神经网络滑模的多功能柔性多状态开关控制方法 [J]. | 电气技术 , 2024 , 25 (5) : 11-21 . |
| MLA | 廖江华 et al. "基于递归径向基神经网络滑模的多功能柔性多状态开关控制方法" . | 电气技术 25 . 5 (2024) : 11-21 . |
| APA | 廖江华 , 高伟 , 唐钧益 , 杨耿杰 . 基于递归径向基神经网络滑模的多功能柔性多状态开关控制方法 . | 电气技术 , 2024 , 25 (5) , 11-21 . |
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负荷开关在动作过程中可能会发生卡涩现象,其储能电动机电流形态能有效反映开关的机械状态.因此,本文提出一种基于改进动态时间规整(IDTW)的负荷开关卡涩故障检测方法.首先,利用滑动均值滤波实时处理电动机电流信号,滤除干扰信号.其次,制定动作电流的录波启动和停止判据,以确保记录完整的电动机动作电流.随后,通过距离公式调整、算法加速和存储空间优化对动态时间规整(DTW)进行改进.以开关正常状态的电流信号为标准波形,利用IDTW计算对比波形与标准波形的标准化距离,并制定边界阈值实现对开关状态的辨识.最后,设计一套在线诊断终端,实现所提算法的工程化.实验结果表明,所提方法具有较强的适应性,对两种型号的负荷开关均能实现正确录波,辨识准确率分别达到 99%和 99.37%,所设计的在线诊断终端能够在较短时间内完成对负荷开关卡涩状态的辨识.
Keyword ：
动态时间规整(DTW) 动态时间规整(DTW) 卡涩故障 卡涩故障 存储空间优化 存储空间优化 工程化实现 工程化实现 负荷开关 负荷开关
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| GB/T 7714 | 黄海燕 , 高伟 , 邱仕达 et al. 基于改进动态时间规整的直流电动机驱动负荷开关卡涩故障辨识 [J]. | 电气技术 , 2024 , 25 (6) : 31-38,55 . |
| MLA | 黄海燕 et al. "基于改进动态时间规整的直流电动机驱动负荷开关卡涩故障辨识" . | 电气技术 25 . 6 (2024) : 31-38,55 . |
| APA | 黄海燕 , 高伟 , 邱仕达 , 杨耿杰 . 基于改进动态时间规整的直流电动机驱动负荷开关卡涩故障辨识 . | 电气技术 , 2024 , 25 (6) , 31-38,55 . |
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针对光伏直流电弧故障定位问题,本文通过研究故障电弧的电磁辐射特性,提出一种基于冗余天线阵列和加权质心算法的定位方法.先计算电弧燃烧时天线采集到的电磁信号的方均根值,与辐照度一起输入BP神经网络预测天线与电弧的距离;再构造冗余天线阵列研究不同天线数量和布局方式,选出接收信号最强的天线,将天线坐标和距离输入加权质心算法,获得定位结果;最后结合K均值聚类算法提高定位精度.实验结果表明,所提方法具有良好的定位能力.
Keyword ：
光伏系统 光伏系统 冗余天线阵列 冗余天线阵列 加权质心算法 加权质心算法 电弧故障定位 电弧故障定位
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| GB/T 7714 | 林亮世 , 高伟 , 杨耿杰 . 基于冗余天线阵列和加权质心算法的光伏系统直流电弧故障定位方法 [J]. | 电气技术 , 2024 , 25 (4) : 16-23,31 . |
| MLA | 林亮世 et al. "基于冗余天线阵列和加权质心算法的光伏系统直流电弧故障定位方法" . | 电气技术 25 . 4 (2024) : 16-23,31 . |
| APA | 林亮世 , 高伟 , 杨耿杰 . 基于冗余天线阵列和加权质心算法的光伏系统直流电弧故障定位方法 . | 电气技术 , 2024 , 25 (4) , 16-23,31 . |
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To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation, a control method involving flexible multistate switches (FMSs) is proposed in this study. This approach is based on an improved double-loop recursive fuzzy neural network (DRFNN) sliding mode, which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults. First, an improved DRFNN sliding mode control (SMC) method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system. To improve the robustness of the system, an adaptive parameter-adjustment strategy for the DRFNN is designed, where its dynamic mapping capabilities are leveraged to improve the transient compensation control. Additionally, a quasi-continuous secondorder sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability. The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem. A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink. The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.
Keyword ：
Distribution networks Distribution networks Double -loop recursive fuzzy Double -loop recursive fuzzy Flexible multistate switch Flexible multistate switch Grounding fault arc suppression Grounding fault arc suppression neural network neural network Quasi -continuous second -order sliding mode Quasi -continuous second -order sliding mode
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| GB/T 7714 | Liao, Jianghua , Gao, Wei , Yang, Yan et al. Control method based on DRFNN sliding mode for multifunctional flexible multistate switch [J]. | GLOBAL ENERGY INTERCONNECTION-CHINA , 2024 , 7 (2) : 190-205 . |
| MLA | Liao, Jianghua et al. "Control method based on DRFNN sliding mode for multifunctional flexible multistate switch" . | GLOBAL ENERGY INTERCONNECTION-CHINA 7 . 2 (2024) : 190-205 . |
| APA | Liao, Jianghua , Gao, Wei , Yang, Yan , Yang, Gengjie . Control method based on DRFNN sliding mode for multifunctional flexible multistate switch . | GLOBAL ENERGY INTERCONNECTION-CHINA , 2024 , 7 (2) , 190-205 . |
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In general, residual current devices (RCDs) have problems such as protection dead-zone and difficulty in threshold setting. A new method for identification of biological electric shock (BES) in low-voltage distribution network based on threshold method is proposed. Firstly, the total residual current of the circuit is denoised by Kalman filter, and then two threshold methods are investigated to determine the electric shock (ES) event and type respectively. Specifically, the first threshold consists of the maximum and average value of the current changes in the previous period, which is an adaptive value of dynamic change. If the current sampling value exceeds the threshold for 10 times in total within 5 ms, an ES is considered to have occurred. Then considering that the amplitude of the waveform of the first three periods after BES has the characteristics of gradual changes, the sampling values of the three periods are recorded. The second threshold is a fixed threshold which is obtained by weighting the phase point changes corresponding to the second-period and the third-period waveforms, and then the specific ES types are distinguished. The proposed method is implemented on hardware devices and analyzed in various common ES situations. The results show that for the three cycles of waveforms collected after the occurrence of grounding or ES, the accuracy of this method is 97.84% and the recognition time is 2.07 ms. In addition, based on the analysis of the actual BES data, a simple digital model is proposed to simulate the actual biological response, and it can be of great help in the subsequent study of such problems.
Keyword ：
adaptive startup threshold adaptive startup threshold biological electric shock(BES) biological electric shock(BES) Covariance matrices Covariance matrices Distribution networks Distribution networks Electric shock Electric shock Fibrillation Fibrillation Kalman filters Kalman filters Low voltage Low voltage Power system reliability Power system reliability Residual current device(RCD) Residual current device(RCD) weighted sum of deviation weighted sum of deviation
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| GB/T 7714 | Yang, Gengjie , Quan, Shengxin , Gao, Wei . A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network [J]. | IEEE TRANSACTIONS ON POWER DELIVERY , 2023 , 38 (3) : 1558-1568 . |
| MLA | Yang, Gengjie et al. "A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network" . | IEEE TRANSACTIONS ON POWER DELIVERY 38 . 3 (2023) : 1558-1568 . |
| APA | Yang, Gengjie , Quan, Shengxin , Gao, Wei . A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network . | IEEE TRANSACTIONS ON POWER DELIVERY , 2023 , 38 (3) , 1558-1568 . |
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