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学者姓名：洪翠
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Abstract ：
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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微机继电保护作为电网安稳运行的关键环节,需要符合新工科背景和工程教育专业认证人才培养要求的教学方法.为此提出一种融合STEM与OBE理念的"微机继电保护"案例教学法,对"微机继电保护"复杂理论体系进行了模块化研究,并以工程案例为载体阐明微机继电保护运行机理,实现教学主体由教师向学生的转变.实践证明,所提方法能够有效提高教学效率,确保工程能力产出,有助于"微机继电保护"课程建设.
Keyword ：
多教育理念融合 多教育理念融合 微机继电保护 微机继电保护 模块化案例教学 模块化案例教学
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| GB/T 7714 | 陈飞雄 , 蔡明杰 , 邵振国 et al. 融合STEM与OBE理念的"微机继电保护"案例教学 [J]. | 电气电子教学学报 , 2024 , 46 (1) : 9-15 . |
| MLA | 陈飞雄 et al. "融合STEM与OBE理念的"微机继电保护"案例教学" . | 电气电子教学学报 46 . 1 (2024) : 9-15 . |
| APA | 陈飞雄 , 蔡明杰 , 邵振国 , 洪翠 , 张宁 . 融合STEM与OBE理念的"微机继电保护"案例教学 . | 电气电子教学学报 , 2024 , 46 (1) , 9-15 . |
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Feeder detection for single-phase ground fault (SPGF) is challenging in a resonant grounded system due to the difference in feeder capacitance to ground and the influence of the arc suppression coil. This article uses semantic segmentation algorithms to implement feeder detection for SPGF in distribution networks. The proposed method overlays transient zero-sequence voltage (ZSV) derivatives and transient zero-sequence current (ZSC) waveforms on the same image. Then, a semantic segmentation algorithm is used to classify the pixel points of the image. The segmentation map output by the semantic segmentation algorithm contains category prediction results for each pixel in the input image. Detecting faulty feeders based on the number of pixels of different categories in the segmentation map can make the final decision-making process more transparent and easy to understand. The validity and adaptability of the proposed method have been confirmed through tests using both simulation and field data. The proposed method achieves an accuracy of over 95% on simulated data, even in the presence of noise interference and asynchronous sampling, etc. The proposed method, furthermore, achieves an accuracy of over 99% when applied to full-scale test data.
Keyword ：
Artificial intelligence Artificial intelligence distribution network distribution network feeder detection feeder detection semantic segmentation semantic segmentation single-phase ground fault (SPGF) single-phase ground fault (SPGF)
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| GB/T 7714 | Hong, Cui , Qiu, Heng-Yi , Gao, Jian-Hong et al. Semantic Segmentation-Based Intelligent Threshold-Free Feeder Detection Method for Single-Phase Ground Fault in Distribution Networks [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Hong, Cui et al. "Semantic Segmentation-Based Intelligent Threshold-Free Feeder Detection Method for Single-Phase Ground Fault in Distribution Networks" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Hong, Cui , Qiu, Heng-Yi , Gao, Jian-Hong , Lin, Shuyue , Guo, Mou-Fa . Semantic Segmentation-Based Intelligent Threshold-Free Feeder Detection Method for Single-Phase Ground Fault in Distribution Networks . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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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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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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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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