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学者姓名：高伟
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目前剩余电流动作保护装置(RCDs)仅依靠固定阈值作为动作判据,在参数配合整定不合理、谐波含量大和高频电弧脉冲等因素的影响下,存在拒动和误动的风险,且无法有效辨识出真正的触电事件.对此,提出了一种基于小波包分解和特征分量动态优选的新型RCD动作判据,可快速识别出常规接地故障、触电、电弧等多种类型的故障.首先,利用高阶统计量中对信号冲击敏感的峭度值捕捉故障起始时刻,并通过计算该时刻前后各一周波差分剩余电流信号的能量比,以实时甄别异常状态.其次,收集故障前一周波和故障启动后三周波的差分剩余电流信号进行小波包分解,融合各节点分量的峭度值、小波包能量比与样本熵特征为动态优选指标(DOI),并结合各分量DOI的贡献度重构低频与高频信号,以突出各故障类型在不同频段电流波形中的故障特征信息.最后,提取不同重构信号的电气量特征,透过双层链式规则实现故障精准分类.该方法已在RCD样机上进行验证,实验结果表明,其在低压交流配电网的串联电弧、接地电弧、触电故障以及常规接地故障检测中表现优异,识别率达到97.52%,平均诊断时间为79.6 ms,能够满足RCDs所要求的灵敏性和可靠性,有效提升了RCDs的实际应用价值.
Keyword ：
串联电弧 串联电弧 剩余电流动作保护装置 剩余电流动作保护装置 小波包分解 小波包分解 特征分量动态优选 特征分量动态优选 触电故障 触电故障
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| GB/T 7714 | 高伟 , 陈渊隆 , 黄天富 . 一种基于小波包分解和特征分量动态优选的剩余电流动作保护方法 [J]. | 仪器仪表学报 , 2025 , 46 (1) : 311-323 . |
| MLA | 高伟 等. "一种基于小波包分解和特征分量动态优选的剩余电流动作保护方法" . | 仪器仪表学报 46 . 1 (2025) : 311-323 . |
| APA | 高伟 , 陈渊隆 , 黄天富 . 一种基于小波包分解和特征分量动态优选的剩余电流动作保护方法 . | 仪器仪表学报 , 2025 , 46 (1) , 311-323 . |
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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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针对大部分光伏电站电弧故障历史数据缺乏的问题,本文在采集电弧超声信号并分析其特点后,提出一种基于超声波传感器与孤立森林的光伏系统串联电弧故障诊断方法.首先,利用S变换将发生串联电弧故障时的超声波暂态电压信号转化至时频域;接着,利用Teager能量算子放大频谱差异性,并通过时频熵提取电弧故障时频域特征;最后,基于动态阈值与孤立森林实现电弧故障诊断且无需历史数据.实验结果表明,所提方法能准确识别串联电弧故障,诊断准确率达到 97.25%,且具备较强的抗干扰能力.
Keyword ：
S变换 S变换 光伏系统 光伏系统 孤立森林 孤立森林 电弧故障诊断 电弧故障诊断 超声波信号 超声波信号
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| GB/T 7714 | 黄晨昊 , 高伟 . 基于超声波传感器与孤立森林的光伏系统串联电弧故障诊断方法 [J]. | 电气技术 , 2025 , 26 (5) : 10-16,26 . |
| MLA | 黄晨昊 et al. "基于超声波传感器与孤立森林的光伏系统串联电弧故障诊断方法" . | 电气技术 26 . 5 (2025) : 10-16,26 . |
| APA | 黄晨昊 , 高伟 . 基于超声波传感器与孤立森林的光伏系统串联电弧故障诊断方法 . | 电气技术 , 2025 , 26 (5) , 10-16,26 . |
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In order to address the challenges posed by weak and variable high-impedance fault signals and limited data availability in practical distribution networks, a novel method for detecting high-impedance faults is proposed. Initially, a multi-head variational autoencoder model based on squeeze-excitation networks is employed to augment the small sample dataset. Subsequently, the data are filtered, and the temporal and frequency domain features are extracted, respectively. Considering the weak characteristics of high impedance fault features and the limitations of the proliferation model in generating comprehensive and effective fault features, a categorical boosting algorithm based on the gradient harmonized mechanism (GHM-CatBoost) is introduced. The GHM-CatBoost algorithm incorporates a gradient harmonized mechanism loss function to address the imbalance in attention between easily distinguishable and challenging samples, thereby mitigating the issue of overfitting. The research findings suggest that the data proliferation model can produce fault samples with a blend of simulation data diversity and measured data randomness, thereby enhancing the richness of the dataset. Furthermore, the fault recognition accuracy achieved by the proposed GHM-CatBoost model is notably high at 97.21%, outperforming its counterpart classifier model. Moreover, the efficacy of the proposed approach is validated through rigorous testing and comparative analysis. © 2025 Science Press. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Fault detection Fault detection Frequency domain analysis Frequency domain analysis Image segmentation Image segmentation Network coding Network coding Variational techniques Variational techniques
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| GB/T 7714 | Gao, Wei , He, Wenxiu , Guo, Moufa et al. Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements [J]. | High Voltage Engineering , 2025 , 51 (3) : 1135-1144 . |
| MLA | Gao, Wei et al. "Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements" . | High Voltage Engineering 51 . 3 (2025) : 1135-1144 . |
| APA | Gao, Wei , He, Wenxiu , Guo, Moufa , Bai, Hao . Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements . | High Voltage Engineering , 2025 , 51 (3) , 1135-1144 . |
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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 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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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 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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针对配电站房缺乏健康评估机制、运维周期设置不合理的问题,提出了一种考虑群体决策差异冲突解决机制的配电站房健康状态综合评估方法.首先,建立配电站房指标体系和专家评价框架,设计了一种新型的二元冲突测量函数来量化全局冲突.然后,使用专家评价结果的虚假度、可信度、可用度等测度指标构造专家修正因子,以改进 D-S 证据理论,通过聚合不同专家的评价意见来量化评价指标的权重.接着,建立改进灰色关联度-逼近理想解法(grey relation analysis-technique for order preference by similarity to an ideal solution,GRA-TOPSIS)评估模型,引入灰色关联接近度,与距离接近度融合得到综合接近度,改善TOPSIS 评价判据片面性的缺陷.最后,计算每个配电站房的评价值与理想解之间的综合接近度,反映配电站房的健康状态.实验分析表明该方法能兼容专家评价之间的冲突性、差异性、不确定性,与现有方法相比评估结果更具准确性和合理性,对运维人员制定合理的检修决策具有一定的指导价值.
Keyword ：
专家修正因子 专家修正因子 专家评价框架 专家评价框架 改进D-S证据理论 改进D-S证据理论 改进GRA-TOPSIS评估方法 改进GRA-TOPSIS评估方法 配电站房 配电站房
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| GB/T 7714 | 罗昆 , 高伟 , 洪翠 . 考虑群体决策差异冲突解决机制的配电站房健康状态评估方法 [J]. | 电力系统保护与控制 , 2024 , 52 (10) : 167-178 . |
| MLA | 罗昆 et al. "考虑群体决策差异冲突解决机制的配电站房健康状态评估方法" . | 电力系统保护与控制 52 . 10 (2024) : 167-178 . |
| APA | 罗昆 , 高伟 , 洪翠 . 考虑群体决策差异冲突解决机制的配电站房健康状态评估方法 . | 电力系统保护与控制 , 2024 , 52 (10) , 167-178 . |
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