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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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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 et al. "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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The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability. © 2024 The Author(s)
Keyword ：
Autoencoder (AE) Autoencoder (AE) Backpropagation neural network (BPNN) Backpropagation neural network (BPNN) Discrete wavelet transform (DWT) Discrete wavelet transform (DWT) Electric shock fault identification Electric shock fault identification Low-voltage power distribution networks Low-voltage power distribution networks Residual current devices (RCDs) Residual current devices (RCDs)
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| GB/T 7714 | Zhang, B. , Guo, S. , Wu, S. et al. Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems [J]. | International Journal of Electrical Power and Energy Systems , 2024 , 161 . |
| MLA | Zhang, B. et al. "Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems" . | International Journal of Electrical Power and Energy Systems 161 (2024) . |
| APA | Zhang, B. , Guo, S. , Wu, S. , Gao, W. . Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems . | International Journal of Electrical Power and Energy Systems , 2024 , 161 . |
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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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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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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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This paper presents a novel approach that simultaneously enables photovoltaic (PV) inversion and flexible arc suppression during single-phase grounding faults. Inverters compensate for ground currents through an arc-elimination function, while outputting a PV direct current (DC) power supply. This method effectively reduces the residual grounding current. To reduce the dependence of the arc-suppression performance on accurate compensation current-injection models, an adaptive fuzzy neural network imitating a sliding mode controller was designed. An online adaptive adjustment law for network parameters was developed, based on the Lyapunov stability theorem, to improve the robustness of the inverter to fault and connection locations. Furthermore, a new arc-suppression control exit strategy is proposed to allow a zerosequence voltage amplitude to quickly and smoothly track a target value by controlling the nonlinear decrease in current and reducing the regulation time. Simulation results showed that the proposed method can effectively achieve fast arc suppression and reduce the fault impact current in single-phase grounding faults. Compared to other methods, the proposed method can generate a lower residual grounding current and maintain good arc-suppression performance under different transition resistances and fault locations.
Keyword ：
Adaptive control Adaptive control Exit strategy Exit strategy Flexible arc suppression Flexible arc suppression Fuzzy neural network Fuzzy neural network Photovoltaic inverter Photovoltaic inverter Sliding mode control Sliding mode control
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| GB/T 7714 | Tang, Junyi , Gao, Wei . A novel cascaded H-bridge photovoltaic inverter with flexible arc suppression function [J]. | GLOBAL ENERGY INTERCONNECTION-CHINA , 2024 , 7 (4) : 513-527 . |
| MLA | Tang, Junyi et al. "A novel cascaded H-bridge photovoltaic inverter with flexible arc suppression function" . | GLOBAL ENERGY INTERCONNECTION-CHINA 7 . 4 (2024) : 513-527 . |
| APA | Tang, Junyi , Gao, Wei . A novel cascaded H-bridge photovoltaic inverter with flexible arc suppression function . | GLOBAL ENERGY INTERCONNECTION-CHINA , 2024 , 7 (4) , 513-527 . |
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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 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 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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