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学者姓名：洪翠
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本文提出一种结合电流积分变化趋势和时序卷积网络(TCN)-支持向量机(SVM)的直流配电网故障定位方法,以区分故障类型并实现直流配电网故障准确定位,为实现直流配电网保护奠定基础.首先计算故障电流的积分序列,并用变分模态分解(VMD)算法分解积分序列,将分解所得高频固有模态函数的特征量作为 TCN与 SVM组合模型的输入特征向量,实现故障线路定位和故障类型判定.仿真结果表明,该方法能快速定位故障线路,准确识别不同故障,并且有较好的适应性和具备一定的抗干扰能力.
Keyword ：
变分模态分解(VMD) 变分模态分解(VMD) 时序卷积网络(TCN) 时序卷积网络(TCN) 电流积分趋势 电流积分趋势 直流配电网故障定位 直流配电网故障定位
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| GB/T 7714 | 祝光思涵 , 洪翠 . 基于电流积分与时序卷积网络-支持向量机的直流配电网故障定位 [J]. | 电气技术 , 2025 , 26 (2) : 1-13 . |
| MLA | 祝光思涵 等. "基于电流积分与时序卷积网络-支持向量机的直流配电网故障定位" . | 电气技术 26 . 2 (2025) : 1-13 . |
| APA | 祝光思涵 , 洪翠 . 基于电流积分与时序卷积网络-支持向量机的直流配电网故障定位 . | 电气技术 , 2025 , 26 (2) , 1-13 . |
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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 等. "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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The technology for identifying birds around power towers using cameras alone is still susceptible to environmental interference. This paper proposes a new bird damage recognition network, RCVNet, which addresses this issue by fusing radio-frequency (RF) images and visual images. The network employs a feature layer fusion approach that accurately identifies bird damages in the monitoring area. Initially, RCVNet takes a group of RF and visual images as input. Then, through a series of convolutional neural networks (CNNs), birds are identified and located. To overcome challenges in recognizing small targets, several improved modules such as crosssupervised fusion network (CSF-net), posture deformable convolution (PDF), small-target attention fusion mechanism (SAFM), and Tiny-YOLOHead are introduced throughout RCVNet, improving surface information utilization and small feature retention rates. Finally, a bird damage discrimination strategy is developed based on the recognition outcomes of birds. As there is currently no public dataset available for RCVNet training, a new bird dataset called CRB2022, which includes RF and visual images, was gathered. Through large-scale experiments utilizing these methods, RCVNet effectively identifies birds, achieving a mean average precision of 79.34% and a mean average recall of 83.29%. Additionally, the discrimination rate of the utilized strategy can reach up to 98%.
Keyword ：
Deep convolutional neural network Deep convolutional neural network RF image RF image Sensor fusion Sensor fusion Target recognition Target recognition Visual image Visual image
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| GB/T 7714 | Gao, Wei , Wu, Yangming , Hong, Cui et al. RCVNet: A bird damage identification network for power towers based on fusion of RF images and visual images [J]. | ADVANCED ENGINEERING INFORMATICS , 2023 , 57 . |
| MLA | Gao, Wei et al. "RCVNet: A bird damage identification network for power towers based on fusion of RF images and visual images" . | ADVANCED ENGINEERING INFORMATICS 57 (2023) . |
| APA | Gao, Wei , Wu, Yangming , Hong, Cui , Wai, Rong-Jong , Fan, Cheng-Tao . RCVNet: A bird damage identification network for power towers based on fusion of RF images and visual images . | ADVANCED ENGINEERING INFORMATICS , 2023 , 57 . |
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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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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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In distribution networks, a single-line-to-ground fault frequently occurs, which poses a risk to the safe operation of power equipment and causes significant property damage. The conventional arc suppression coil serves as a passive compensating device, which is utilized to compensate for the capacitive component of the fault current. However, it lacks the ability to compensate for an active component of the fault current. To address this issue, this paper proposes a cascaded H-bridge inverter based on a backstepping control method with DC suppression. This method aims to fully compensate for the ground-fault current and restrict the faulty phase voltage. The proposed approach effectively mitigates the impact of system parameter perturbations, suppresses the DC component of the injected current, and reliably limits the fault current. Through simulation verification with different fault resistances, the results indicate the feasibility of the proposed method. ©2023 IEEE.
Keyword ：
Backstepping Backstepping Bridge circuits Bridge circuits Electric grounding Electric grounding Electric inverters Electric inverters Electric power distribution Electric power distribution
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| GB/T 7714 | Jiao, Yaohui , Zheng, Zeyin , Guo, Moufa et al. Design and Analysis of Backstepping Controller with DC Suppression Loop for Flexible Arc Suppression System in Distribution Networks [C] . 2023 : 255-258 . |
| MLA | Jiao, Yaohui et al. "Design and Analysis of Backstepping Controller with DC Suppression Loop for Flexible Arc Suppression System in Distribution Networks" . (2023) : 255-258 . |
| APA | Jiao, Yaohui , Zheng, Zeyin , Guo, Moufa , Hong, Cui . Design and Analysis of Backstepping Controller with DC Suppression Loop for Flexible Arc Suppression System in Distribution Networks . (2023) : 255-258 . |
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There is a risk of fire caused by series arc failure in the operation of photovoltaic (PV) system. Therefore, it is required to discuss a solution for rapid arc fault detection. To address the series arc fault (SAF) detection under different working conditions, a method based on squeeze-and-excitation (SE)-inception multi-input convolutional neural network (MICNN) is proposed. Firstly, normalization and Hankel-singular value decomposition algorithm are used to denoise the current, which effectively avoid the influence of switching frequency on the subsequent diagnostic accuracy. Subsequently, the filtered time-domain signal and the frequency-domain signal after Fourier transform are input into a variant one-dimensional convolutional neural network (1D-CNN) model for training and testing. The proposed model is characterized by transforming the traditional CNN into MICNN, and introducing the inception network with spatial scaling function and the SE network structure with channel attention mechanism. Extensive simulations are performed to evaluate the efficacy with a desirable result of 97.48%, which is superior to traditional methods such as CNN, wavelet decomposition, and mathematical statistics. The proposed method can not only detect arc faults occurring in different locations, but also resist the disturbance of dynamic shading, maximum power point tracking (MPPT), strong wind, etc. In addition, this model achieved satisfactory results in three cases of long line fault, single series and array ageing.
Keyword ：
Hankel-singular value decomposition (Hankel-SVD) Hankel-singular value decomposition (Hankel-SVD) (MICNN) (MICNN) Multi-input convolutional neural network  Multi-input convolutional neural network  Photovoltaic (PV) system Photovoltaic (PV) system Series arc fault (SAF) Series arc fault (SAF) Squeeze-and-excitation-inception (SE-Inception) Squeeze-and-excitation-inception (SE-Inception)
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| GB/T 7714 | Chen, Xiaoqi , Gao, Wei , Hong, Cui et al. A novel series arc fault detection method for photovoltaic system based on multi-input neural network [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 140 . |
| MLA | Chen, Xiaoqi et al. "A novel series arc fault detection method for photovoltaic system based on multi-input neural network" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 140 (2022) . |
| APA | Chen, Xiaoqi , Gao, Wei , Hong, Cui , Tu, Yanzhao . A novel series arc fault detection method for photovoltaic system based on multi-input neural network . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 140 . |
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In order to quickly detect and reliably identify fault of DC distribution network, a fault detection scheme based on IEWT(Improved Empirical Wavelet Transform) and IMDMF(Improved Multi-view Deep Matrix Factorization) is proposed. The local phase-frequency spectra function of fault current is fitted nonlinearly by least square method, based on which, phase-frequency response of empirical wavelet function is modified under certain conditions to match the phase-frequency spectra characteristics of fault current as much as possible. The IEWT is used to decompose the current, and the modulus maximum of detail component c3 is calculated to construct the fault detection criterion. A weighted self-learning network is designed, according to the importance of the data to the classification task, different weights are allocated and nested in the front of the multi-view deep matrix factorization model. The fault features are extracted from the current component c1, c2, and c3, and the inter electrode voltage udc by using the IMDMF, and the fault classification is realized by the soft distribution layer. The results of simulation test show that the proposed fault detection scheme can meet the requirements of speed and reliability for fault detection, and the fault classification accuracy is high, which lays a good foundation for subsequent fault processing. © 2022, Electric Power Automation Equipment Press. All right reserved.
Keyword ：
Fault detection Fault detection Frequency response Frequency response Least squares approximations Least squares approximations Matrix algebra Matrix algebra Matrix factorization Matrix factorization Spectroscopy Spectroscopy Wavelet transforms Wavelet transforms
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| GB/T 7714 | Hong, Cui , Lian, Shuting , Huang, Sheng et al. Fault detection scheme based on IEWT and IMDMF for DC distribution network [J]. | Electric Power Automation Equipment , 2022 , 42 (6) : 8-15 and 29 . |
| MLA | Hong, Cui et al. "Fault detection scheme based on IEWT and IMDMF for DC distribution network" . | Electric Power Automation Equipment 42 . 6 (2022) : 8-15 and 29 . |
| APA | Hong, Cui , Lian, Shuting , Huang, Sheng , Guo, Moufa . Fault detection scheme based on IEWT and IMDMF for DC distribution network . | Electric Power Automation Equipment , 2022 , 42 (6) , 8-15 and 29 . |
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In order to locate the line where single pole grounding or pole to pole short circuit fault has occurred in a DC distribution network quickly, a location scheme is proposed and it is on the based of the Cos-similarity of current integrals. The DC bus current was transformed with fast Fourier firstly, then energy entropy of the transform was calculated, and it was used as the fault detection criterion. The fault location algorithm was activated when the fault detection criterion was satisfied, and then integral calculation of the currents before and after the fault at both ends of all DC distribution line were carried out. Then the Cos-similarity of current integrals were calculated. Location of the fault line and selection of pole were according to the comparison of the calculation results.Simulation results show that the scheme can locate the faults quickly and accurately in both of single pole grounding and pole to pole short circuit, which lays a good foundation for subsequent fault processing. © 2022 Editorial Department of Electric Machines and Control. All rights reserved.
Keyword ：
Electric grounding Electric grounding Entropy Entropy Fast Fourier transforms Fast Fourier transforms Fault detection Fault detection Location Location Poles Poles
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| GB/T 7714 | Hong, Cui , Lin, Chang , Gao, Wei et al. Application of Cos-similarity of current integrals on the location of fault line in a DC distribution networks [J]. | Electric Machines and Control , 2022 , 26 (8) : 88-99 . |
| MLA | Hong, Cui et al. "Application of Cos-similarity of current integrals on the location of fault line in a DC distribution networks" . | Electric Machines and Control 26 . 8 (2022) : 88-99 . |
| APA | Hong, Cui , Lin, Chang , Gao, Wei , Guo, Mou-Fa . Application of Cos-similarity of current integrals on the location of fault line in a DC distribution networks . | Electric Machines and Control , 2022 , 26 (8) , 88-99 . |
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In order to solve the problems of unsatisfactory diagnosis performance and unstable model of conventional fault diagnosis methods for transformers, a new approach based on improved empirical wavelet transform (IEWT) and salp swarm algorithm (SSA) optimized kernel extreme learning machine (KELM) is proposed in this study. Firstly, IEWT is used to adaptively decompose the vibration signal to obtain a set of empirical wavelet functions (EWFs). Secondly, the first n-order components with high correlation coefficient are collected. Thirdly, the mean value, variance, kurtosis, refine composite multiscale entropy (RCMSE), and time-frequency entropy(TFE) of these n order components are calculated to construct a fusion feature vector. Finally, a two-level diagnostic model based on SSA-KELM is established. The first-level of it is applied to identify normal and abnormal states, and the second-level is selected to identify fault categories in the abnormal states. The proposed method can effectively diagnose the existing fault categories in the training set and accurately identify the unknown categories of faults. Experimental results show that the proposed method can efficiently extract features of different vibration signals and identify the faults, with an average classification accuracy of 96.25%. It is better than other methods, such as wavelet packet energy spectrum analysis-KELM and EWT-fisher.
Keyword ：
Fault diagnosis Fault diagnosis Improved empirical wavelet transform (IEWT) Improved empirical wavelet transform (IEWT) Kernel extreme learning machine (KELM) Kernel extreme learning machine (KELM) Salp swarm algorithm (SSA) Salp swarm algorithm (SSA) Transformer Transformer
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| GB/T 7714 | Lu, Sijia , Gao, Wei , Hong, Cui et al. A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine [J]. | ADVANCED ENGINEERING INFORMATICS , 2021 , 49 . |
| MLA | Lu, Sijia et al. "A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine" . | ADVANCED ENGINEERING INFORMATICS 49 (2021) . |
| APA | Lu, Sijia , Gao, Wei , Hong, Cui , Sun, Yiqun . A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine . | ADVANCED ENGINEERING INFORMATICS , 2021 , 49 . |
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