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学者姓名：杨耿杰
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Abstract ：
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 time-frequency 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 dry-type 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. © 2024 Elsevier Ltd
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, H. , Gao, W. , Yang, G. . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults [J]. | Measurement: Journal of the International Measurement Confederation , 2024 , 238 . |
| MLA | Huang, H. 等. "Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults" . | Measurement: Journal of the International Measurement Confederation 238 (2024) . |
| APA | Huang, H. , Gao, W. , Yang, G. . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults . | Measurement: Journal of the International Measurement Confederation , 2024 , 238 . |
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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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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 (05) : 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 . 05 (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 (05) , 11-21 . |
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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 (04) : 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 . 04 (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 (04) , 16-23,31 . |
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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 (06) : 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 . 06 (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 (06) , 31-38,55 . |
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本发明涉及一种不对称配电网的多功能补偿方法，该方法以无独立直流源的四桥臂级联H桥变流器为多功能变流器，以分序控制为多功能变流器的控制策略，包括无功补偿电流目标值计算方法、三相对地参数不对称补偿电流计算方法、三相桥臂变流器接地故障补偿电流计算方法及其直流侧电容稳压电流的相间控制方法、接地桥臂变流器接地故障补偿电流计算方法及其直流侧电容稳压电压计算方法，实现无功功率补偿、接地故障补偿和不对称电流补偿。该方法对于设备的利用率高，实现成本低，且补偿效果全面，具有更好的故障抑制性能。
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| GB/T 7714 | 郭谋发 , 游建章 , 高伟 et al. 一种不对称配电网的多功能补偿方法 : CN202111510910.0[P]. | 2021-12-10 00:00:00 . |
| MLA | 郭谋发 et al. "一种不对称配电网的多功能补偿方法" : CN202111510910.0. | 2021-12-10 00:00:00 . |
| APA | 郭谋发 , 游建章 , 高伟 , 洪翠 , 杨耿杰 . 一种不对称配电网的多功能补偿方法 : CN202111510910.0. | 2021-12-10 00:00:00 . |
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本发明提出一种配电网单相接地故障柔性融合消弧方法，采用级联H桥变流器为柔性融合消弧装置，基于电流电压双闭环控制器，同时以故障点电流和故障点电压为控制目标。所提融合消弧方法无需复杂的切换条件，两种消弧方法同时在一套柔性消弧装置上实现，相较于消弧线圈与消弧柜配合使用的消弧方法，节省了设备的投入以及不同装置间的协同。不仅适用于中性点不接地系统，也适用于中性点经消弧线圈接地系统，且受消弧线圈暂态电流和线路阻抗压降影响小，兼具了柔性电流消弧法和柔性电压消弧法的优势。为柔性消弧技术在不同配电系统中的推广与应用提供了有力的技术保障。
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| GB/T 7714 | 郭谋发 , 游建章 , 高伟 et al. 配电网单相接地故障柔性融合消弧方法 : CN202111344747.5[P]. | 2021-11-12 00:00:00 . |
| MLA | 郭谋发 et al. "配电网单相接地故障柔性融合消弧方法" : CN202111344747.5. | 2021-11-12 00:00:00 . |
| APA | 郭谋发 , 游建章 , 高伟 , 洪翠 , 杨耿杰 , 郑泽胤 . 配电网单相接地故障柔性融合消弧方法 : CN202111344747.5. | 2021-11-12 00:00:00 . |
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When a high impedance fault (HIF) occurs in a distribution network, the detection efficiency of traditional protection devices is strongly limited by the weak fault information. In this study, a method based on S-transform (ST) and average singular entropy (ASE) is proposed to identify HIFs. First, a wavelet packet transform (WPT) was applied to extract the feature frequency band. Thereafter, the ST was investigated in each half cycle. Afterwards, the obtained time-frequency matrix was denoised by singular value decomposition (SVD), followed by the calculation of the ASE index. Finally, an appropriate threshold was selected to detect the HIFs. The advantages of this method are the ability of fine band division, adaptive time-frequency transformation, and quantitative expression of signal complexity. The performance of the proposed method was verified by simulated and field data, and further analysis revealed that it could still achieve good results under different conditions.
Keyword ：
High impedance fault (HIF) High impedance fault (HIF) Singular entropy (SE) Singular entropy (SE) S-transform (ST) S-transform (ST) Wavelet packet transform (WPT) Wavelet packet transform (WPT)
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| GB/T 7714 | Zeng, Xiaofeng , Gao, Wei , Yang, Gengjie . High impedance fault detection in distribution network based on S-transform and average singular entropy [J]. | GLOBAL ENERGY INTERCONNECTION-CHINA , 2023 , 6 (1) : 64-80 . |
| MLA | Zeng, Xiaofeng et al. "High impedance fault detection in distribution network based on S-transform and average singular entropy" . | GLOBAL ENERGY INTERCONNECTION-CHINA 6 . 1 (2023) : 64-80 . |
| APA | Zeng, Xiaofeng , Gao, Wei , Yang, Gengjie . High impedance fault detection in distribution network based on S-transform and average singular entropy . | GLOBAL ENERGY INTERCONNECTION-CHINA , 2023 , 6 (1) , 64-80 . |
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In light of challenges encountered in extracting DC series arc fault features within photovoltaic （PV） system and the observed limitations in algorithm generalization and adaptability，this study introduces a novel series arc fault feature extraction method based on dynamic time warping（DTW）. Initially，the moving average（MA）value of the current signal is calculated to identify the mutation event and collect the abnormal signal. Then，the singular spectrum analysis（SSA）is used to remove the trend component of abnormal signals and reduce the differences between different PV system signals. Following this，the DTW distance of the signal is calculated to extract the valid features. In the end，the waveform factor of the identified feature vector serves as the diagnostic criterion to identify the arc fault，short circuit fault and the interference events caused by inverter start- up and irradiance mutation. The experimental results show that the arc fault identification method based on the proposed feature extraction is not only fast and highly recognizable，but also suitable for different inverter systems，has strong adaptability，and the comprehensive performance is better than that of the comparison method. © 2023 Science Press. All rights reserved.
Keyword ：
dynamic time warping（DTW） dynamic time warping（DTW） electric arcs electric arcs fault detection fault detection photovoltaic system photovoltaic system singular spectrum analysis（SSA） singular spectrum analysis（SSA）
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| GB/T 7714 | Li, X. , Gao, W. , Yang, G. . DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING; [基于动态时间规整的光伏系统直流串联电弧故障特征提取] [J]. | Acta Energiae Solaris Sinica , 2023 , 44 (12) : 82-89 . |
| MLA | Li, X. et al. "DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING; [基于动态时间规整的光伏系统直流串联电弧故障特征提取]" . | Acta Energiae Solaris Sinica 44 . 12 (2023) : 82-89 . |
| APA | Li, X. , Gao, W. , Yang, G. . DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING; [基于动态时间规整的光伏系统直流串联电弧故障特征提取] . | Acta Energiae Solaris Sinica , 2023 , 44 (12) , 82-89 . |
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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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