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学者姓名：高伟
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Abstract ：
油纸绝缘作为电力变压器中的主绝缘设备,在工业生产和电力传输应用中尤为重要,为验证油纸绝缘的性能状态,该文研制了基于介质响应原理的现场可编程电力电子控制实验平台.平台以LabVIEW编程环境和三电极测试装置作为载体,采用状态机框架设计了回复电压谱与极化谱测量流程,并嵌入聚类云模型算法实现油纸绝缘状态精准分类.该实验平台可促进理论知识与实践经验相结合的教学模式革新,满足实验探索、科学研究等多层次需求.
Keyword ：
回复电压测试 回复电压测试 实验平台设计 实验平台设计 数字编程控制 数字编程控制 油纸绝缘 油纸绝缘
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| GB/T 7714 | 邹阳 , 黄煜 , 方梦泓 et al. 基于介质响应原理的变压器油纸绝缘测试实验平台设计 [J]. | 实验技术与管理 , 2025 , 42 (1) : 176-183 . |
| MLA | 邹阳 et al. "基于介质响应原理的变压器油纸绝缘测试实验平台设计" . | 实验技术与管理 42 . 1 (2025) : 176-183 . |
| APA | 邹阳 , 黄煜 , 方梦泓 , 石松浩 , 姚雨佳 , 高伟 . 基于介质响应原理的变压器油纸绝缘测试实验平台设计 . | 实验技术与管理 , 2025 , 42 (1) , 176-183 . |
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Abstract ：
为丰富"电气设备在线监测与故障诊断"课程的实验教学资源,开发了配电变压器潜伏性故障检测实验教学系统,包括配变、振动信号采集装置和上位机软件平台等单元.在此基础上,设计一种新的故障诊断方法,通过变分模态分解实现信号分解,用多尺度排列熵提取故障特征,再经随机森林分类器实现故障分类.该平台用于常规实验外,还预置开放式接口供学生自主开发应用程序,开拓创新思维、提高动手实践能力.
Keyword ：
多尺度排列熵 多尺度排列熵 振动信号采集装置 振动信号采集装置 故障检测 故障检测 配电变压器 配电变压器 随机森林 随机森林
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| GB/T 7714 | 崔凤新 , 卢思佳 , 高伟 . 配电变压器故障检测实验教学系统设计 [J]. | 电气电子教学学报 , 2024 , 46 (5) : 235-240 . |
| MLA | 崔凤新 et al. "配电变压器故障检测实验教学系统设计" . | 电气电子教学学报 46 . 5 (2024) : 235-240 . |
| APA | 崔凤新 , 卢思佳 , 高伟 . 配电变压器故障检测实验教学系统设计 . | 电气电子教学学报 , 2024 , 46 (5) , 235-240 . |
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配电网高阻接地故障（HIF）因故障特征微弱而难以被常规装置检测到，容易引发过电压、火灾和人身触电等安全问题。本文提出一种将时频分解和阈值判断结合的HIF识别方法。首先，利用经验模态分解（EMD）对零序电流进行处理；然后，选取第二个固有模态分量（IMF）作为特征分量并进行高斯滤波；最后，统计特征分量的局部极大值点的个数来判断是否发生HIF。利用PSCAD/EMTDC软件搭建10kV配电网模型进行算法验证。仿真结果表明，所提方法能够以较高的准确率将HIF和一些常见的干扰事件区分开，并且在发生噪声干扰、采样率变化、系统中性点接地方式改变及系统网络结构改变的情况下，均表现出较好的适应性。
Keyword ：
局部极大值点数 局部极大值点数 经验模态分解(EMD) 经验模态分解(EMD) 配电网 配电网 高阻接地故障(HIF) 高阻接地故障(HIF)
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| GB/T 7714 | 唐钧益 , 曾肖枫 , 高伟 . 一种基于经验模态分解和局部极大值点数的配电网高阻接地故障检测方法 [J]. | 电气技术 , 2024 , 25 (06) : 14-23 . |
| MLA | 唐钧益 et al. "一种基于经验模态分解和局部极大值点数的配电网高阻接地故障检测方法" . | 电气技术 25 . 06 (2024) : 14-23 . |
| APA | 唐钧益 , 曾肖枫 , 高伟 . 一种基于经验模态分解和局部极大值点数的配电网高阻接地故障检测方法 . | 电气技术 , 2024 , 25 (06) , 14-23 . |
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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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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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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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To address the existing issue of electric shock incidents that cannot be accurately identified by current leakage protection devices, this paper presents a novel electric shock accident recognition method. Firstly, the method of singular spectrum analysis (SSA) is employed to extract the main components of leakage recording data. Subsequently, 20 temporal domain features of the leakage current waveform are extracted. Then, an ensemble learning model based on extreme gradient boosting (XGBoost), categorical boosting (CatBoost) and random forest (RF), is established to select optimal features that best represent the sample characteristics from the feature set. Finally, support vector machine (SVM) is used to classify the extracted dataset. Experimental results demonstrate that this method can rapidly differentiate between electric shock faults and common leakage faults, achieving an accuracy rate as high as 99%, indicating its feasibility. © 2024 IEEE.
Keyword ：
electric shock faults identification electric shock faults identification feature selection feature selection leakage protection device leakage protection device singular spectrum analysis (SSA) singular spectrum analysis (SSA)
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| GB/T 7714 | Chen, Y.-L. , Gao, W. , Rao, J.-M. et al. Electric Shock Accident Detection Method Based on Ensemble Decision Trees Boosting for Feature Selection [未知]. |
| MLA | Chen, Y.-L. et al. "Electric Shock Accident Detection Method Based on Ensemble Decision Trees Boosting for Feature Selection" [未知]. |
| APA | Chen, Y.-L. , Gao, W. , Rao, J.-M. , Guo, M.-F. , Zheng, Z.-Y. . Electric Shock Accident Detection Method Based on Ensemble Decision Trees Boosting for Feature Selection [未知]. |
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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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The existing residual current device (RCD) operates based on the amplitude of the residual current, but if the threshold is not reasonably set, the RCD is prone to reject or misoperate. Therefore, identifying biological electric-shock faults from grounding faults is a crucial approach. Current research only selects one or several features without following proper feature selection rules. Furthermore, machine learning methods require a certain number of samples to train the model to ensure algorithm accuracy and stability. However, obtaining a large number of biological electric-shock samples is challenging during actual experiments, and the algorithm model cannot learn the waveform in real settings. To solve the above problems, a biological electric-shock fault identification method based on multi-feature optimization selection under unbalanced small samples is proposed. Firstly, variational auto-encoders (VAE) is adopted to multiply the electric-shock small sample data collected by experiments to achieve positive and negative sample balance. Due to the complexity and danger of the scenes, it is difficult to obtain the actual electric-shock samples. The problem of small samples will lead to low accuracy and poor effectiveness of the training model, and the unbalanced samples will lead to deviations in the prediction results of the model, resulting in poor identification accuracy of a few types of samples. Therefore, a few samples are enhanced by introducing VAE to improve the effectiveness of the model. Secondly, 23 features which can reflect the dynamic characteristics of the waveform are extracted in time domain, the optimal expression feature group is selected from them by Gaussian kernel Fisher discriminant analysis (GKFDA) and maximal information coefficient (MIC). Through data analysis, various index features can be extracted from the changing forms of biological electric-shock waveforms. The addition of high-quality features will improve the diagnostic accuracy of the classifier to a certain extent, but the introduction of bad and redundant features will increase the running time of the algorithm and reduce the diagnostic accuracy of the classifier. Therefore, GKFDA and MIC are combined to perform feature scoring for each feature, and the optimal expression feature group is selected intuitively and independently based on the scoring results, which could improve the feature quality and reflect the regularity of feature selection. Finally, a forgetting-factor-based online sequential extreme learning machine (FOS-ELM) algorithm is investigated to identify the electric-shock behavior. There are abundant electric-shock scenes in the real environments. The escape behaviors of living objects during electric shock will have a great influence on the electric-shock waveform, which makes it difficult for the traditional off-line classifier to have adaptability. The online sequential extreme learning machine (OS-ELM) has an online learning mechanism that allows online updates for new samples without the historical data. The forgetting factor is introduced to form FOS-ELM, aiming to further solve the shortcoming of slow learning speed of OS-ELM, so that it can quickly adapt to changes of environmental samples with higher learning efficiency. The experimental data of conventional grounding fault and biological electric-shock fault in 12 scenes were collected for the verification of the proposed algorithm. The results show that the diagnosis accuracy of the proposed model can reach 98.75%, among which all 40 conventional grounding fault samples are correctly judged with an accuracy of 100%, while only 1 of 40 actual biological electric-shock fault samples is wrong with an accuracy of 97.5%. From the perspective of time, the average online learning time is 1.378 ms, and the average diagnosis time is only 1.33 ms. © 2024 China Machine Press. All rights reserved.
Keyword ：
Bioinformatics Bioinformatics Computer aided diagnosis Computer aided diagnosis Data mining Data mining E-learning E-learning Electric grounding Electric grounding Feature extraction Feature extraction Learning algorithms Learning algorithms Time domain analysis Time domain analysis
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| GB/T 7714 | Gao, Wei , Rao, Junmin , Quan, Shengxin et al. Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample [J]. | Transactions of China Electrotechnical Society , 2024 , 39 (7) : 2060-2071 . |
| MLA | Gao, Wei et al. "Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample" . | Transactions of China Electrotechnical Society 39 . 7 (2024) : 2060-2071 . |
| APA | Gao, Wei , Rao, Junmin , Quan, Shengxin , Guo, Moufa . Biological Electric-Shock Fault Identification Method Based on Multi-Feature Optimization Selection under Unbalanced Small Sample . | Transactions of China Electrotechnical Society , 2024 , 39 (7) , 2060-2071 . |
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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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