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学者姓名：杨耿杰
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Abstract ：
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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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 second- order 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. © 2024
Keyword ：
Adaptive control systems Adaptive control systems Electric arcs Electric arcs Electric grounding Electric grounding Electric power distribution Electric power distribution Fuzzy inference Fuzzy inference Fuzzy neural networks Fuzzy neural networks MATLAB MATLAB Sliding mode control Sliding mode control System stability System stability
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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 , 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 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 , 2024 , 7 (2) , 190-205 . |
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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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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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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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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. et al. "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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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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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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由于串联电弧故障特征表现不足以及样本不平衡的问题,导致传统的诊断算法检测效果不佳.提出了一种基于图像识别的光伏阵列串联电弧故障诊断方法:利用格拉姆角和场(GASF)将发生串联电弧故障时的暂态电流数据编码为二维图像,从而放大电弧故障的本质特征;深度卷积生成对抗网络(DCGAN)被用来增扩电弧故障GASF特征图像,以均衡正常与故障样本数量;训练一个LeNet-5诊断模型完成电弧故障的识别.经过实验验证,所提方法有效提升了光伏阵列串联电弧故障的辨识度,且具备优秀的抗干扰能力,对实测数据的整体识别准确率高达99.5％.
Keyword ：
串联电弧故障 串联电弧故障 光伏阵列 光伏阵列 格拉姆角和场 格拉姆角和场 深度卷积生成对抗网络 深度卷积生成对抗网络
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| GB/T 7714 | 金辉 , 高伟 , 杨耿杰 . 一种光伏阵列串联电弧故障智能检测方法 [J]. | 电工电气 , 2023 , (1) : 43-47,66 . |
| MLA | 金辉 et al. "一种光伏阵列串联电弧故障智能检测方法" . | 电工电气 1 (2023) : 43-47,66 . |
| APA | 金辉 , 高伟 , 杨耿杰 . 一种光伏阵列串联电弧故障智能检测方法 . | 电工电气 , 2023 , (1) , 43-47,66 . |
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