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学者姓名：关向雨
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Abstract ：
针对气体绝缘组合电器(Gas Insulated Switchgear, GIS)红外巡检中图像对比度低、易受道路等背景辐射干扰，各组件温升差异难以直接提取的不足，提出了基于多光谱语义分割和目标检测的GIS各组件红外特征智能识别算法。采用红外-可见光双光谱成像系统，构建了现场变电站GIS的红外和可见光多光谱数据集；基于MF-net多光谱语义分割框架，采用双分支编码器分别提取GIS外壳红外和可见光特征信息，进而在译码器上实现多光谱特征融合和GIS本体红外图像分割，从而排除了环境背景辐射对GIS组件识别和温升特性提取的影响；将分割后的GIS本体红外图像进行主母线、分支母线、互感器、断路器和隔离开关等组件标记，随后采用YOLOv4算法实现对GIS不同组件的识别。结果表明与未进行语义分割GIS红外组件识别模型相比，所提出模型可以达84.2%平均识别准确率，其中电流互感器精确度达93.68%、断路器精确度达92.68%、召回率达96.20%和F1值得分0.94,GIS组件温升识别能够去除道路等高辐射背景对测温结果干扰，对提高户外GIS设备温升红外热成像的结果可靠性和智能化具有应用价值。
Keyword ：
GIS GIS MFNet MFNet YOLOv4 YOLOv4 可见光图像 可见光图像 红外图像 红外图像 语义分割 语义分割
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| GB/T 7714 | 王炜斌 , 林熠 , 刘江 et al. 基于多光谱语义分割的GIS组件红外特征识别方法 [J]. | 电气开关 , 2024 , 62 (02) : 37-42,47 . |
| MLA | 王炜斌 et al. "基于多光谱语义分割的GIS组件红外特征识别方法" . | 电气开关 62 . 02 (2024) : 37-42,47 . |
| APA | 王炜斌 , 林熠 , 刘江 , 关向雨 . 基于多光谱语义分割的GIS组件红外特征识别方法 . | 电气开关 , 2024 , 62 (02) , 37-42,47 . |
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Abnormal conditions of field photovoltaic (PV) array such as open-circuit, short-circuit, and partial shading are embedded in DC side voltage/current curves. Besides, meteorological factors as solar radiation, wind speed, and ambient temperature can also influence fault behaviors. To realize abnormal identify of PV array under environmental interference, this paper presents a graph attention network (GAT) based fault detection algorithm for PV array. Fault simulation experiments are conducted on grid-connected PV array, and the voltage/current curves of the PV DC side under different states (normal, open-circuit, short-circuit, and partial shading) were collected to build dataset. One-dimensional convolution and two parallel graph attention layers are adopted to extract temporal and dimensional features of the voltage/current series. A gated recurrent unit (GRU) is employed to capture the long-term dependencies of the time-series data. Fully connected (FC) layers and variational auto-encoder (VAE) are combined optimized for detecting and locating the PV abnormal events. Model performance are compared with Robust Anomaly Detection (OmniAnomaly), Transformer Networks for Anomaly Detection (TranAD), and Long Short-Term Memory (LSTM), result show that the proposed grid-connected PV array fault detection model achieves an accuracy of 96.8% on the test dataset, providing an effective method for fault diagnosis of grid-connected PV systems under different meteorological conditions. © Beijing Paike Culture Commu. Co., Ltd. 2024.
Keyword ：
Anomaly detection Anomaly detection Fault detection Fault detection Feature extraction Feature extraction Long short-term memory Long short-term memory Statistical tests Statistical tests Time series Time series Wind Wind
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| GB/T 7714 | Jiang, Wujie , Fu, Xiaoying , Zhang, Yanfeng et al. Anomaly Detection for Grid-Connected Photovoltaic Array via Graph Attention Mechanism [C] . 2024 : 759-769 . |
| MLA | Jiang, Wujie et al. "Anomaly Detection for Grid-Connected Photovoltaic Array via Graph Attention Mechanism" . (2024) : 759-769 . |
| APA | Jiang, Wujie , Fu, Xiaoying , Zhang, Yanfeng , Xiong, Hengping , Wen, Yihan , Guan, Xiangyu . Anomaly Detection for Grid-Connected Photovoltaic Array via Graph Attention Mechanism . (2024) : 759-769 . |
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High quality 3D reconstruction technique is essential for digital twin (DT) application of power equipment. This work presents a PointNet-MLS combined architecture to realize component segmentation and surface reconstruction of gas insulated switchgear (GIS) with complex background interference. In order to make the GIS ontology point cloud obtained continuous and smooth, greedy projection triangulation is then applied. Lastly, the local features of the GIS point cloud are enhanced, and the three-dimensional geometric properties of the GIS apparatus are better restored using the moving least squares approach. The results show that the mean intersection over union (mIoU) of PointNet++ algorithm for on-site GIS point cloud segmentation can reach 92.1%, which is higher than 32.8% and 13.7% of K-means and PointNet algorithms, respectively. The proposed MLS algorithm can effectively repair the defects of GIS point cloud after greedy projection triangulation, so that the repaired surface part can maintain the three-dimensional shape characteristics of the GIS point cloud. © 2024, Beijing Paike Culture Commu. Co., Ltd.
Keyword ：
Electric switchgear Electric switchgear Image reconstruction Image reconstruction Surface reconstruction Surface reconstruction Triangulation Triangulation
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| GB/T 7714 | Lv, Chaowei , Guan, Xiangyu , Liu, Jiang et al. 3-D Segmentation and Surface Reconstruction of Gas Insulated Switchgear via PointNet-MLS Architecture [C] . 2024 : 187-193 . |
| MLA | Lv, Chaowei et al. "3-D Segmentation and Surface Reconstruction of Gas Insulated Switchgear via PointNet-MLS Architecture" . (2024) : 187-193 . |
| APA | Lv, Chaowei , Guan, Xiangyu , Liu, Jiang , Liao, Jingwen . 3-D Segmentation and Surface Reconstruction of Gas Insulated Switchgear via PointNet-MLS Architecture . (2024) : 187-193 . |
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传统的激光测振仪多使用氦氖激光器，不满足人眼安全条件，在户外容易受到太阳光干扰。其使用的光学天线大多数仅能工作于氦氖激光器的辐射波段，同时存在结构复杂、成本较高、收集效率低、工作距离较短等不足。为了解决以上问题，基于近红外波段透镜组的扩束和聚焦原理，以Zemax作为设计工具，以点列图和波前图为主要分析手段，设计并制作了一款可工作于1 550 nm的三片式收发一体光学天线，结构简单，成本低。分析结果显示：在0.3～15.0 m的工作距离范围内均可获得直径小于2 mm的聚焦光斑；测试结果显示其最大收集效率可达到51.6%。同时，将该光学天线应用于自主设计搭建的激光多普勒测振仪中，实现了不同工作距离下的振动测量，有效扩展了激光测振仪的应用范围和应用场景，在机械、航空、建筑、医学、农学等领域有很大的应用潜力。
Keyword ：
Zemax Zemax 扩束聚焦镜 扩束聚焦镜 激光多普勒测振仪 激光多普勒测振仪
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| GB/T 7714 | 郑帅君 , 谢涛 , 马赛 et al. 激光多普勒测振仪中光学天线的优化设计与测试 [J]. | 武汉工程大学学报 , 2024 , 46 (04) : 431-438 . |
| MLA | 郑帅君 et al. "激光多普勒测振仪中光学天线的优化设计与测试" . | 武汉工程大学学报 46 . 04 (2024) : 431-438 . |
| APA | 郑帅君 , 谢涛 , 马赛 , 张余豪 , 张聪 , 关向雨 et al. 激光多普勒测振仪中光学天线的优化设计与测试 . | 武汉工程大学学报 , 2024 , 46 (04) , 431-438 . |
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为获取交变电磁力作用下气体绝缘封闭金属开关设备（GIS）内部导体与外壳之间机械振动的传递和分布特性，该文采用结构声强法构建了GIS振动能量流动的可视化模型。通过电磁-结构多场耦合方法构建了工频电流激励下的GIS振动功率流模型，并进行了物理模拟试验验证。应用所构建的GIS功率流模型提取了微元的应力张量及速度结果，进而求解了振动能量流的空间分量，并采用空间向量和流线合成的方法实现了GIS腔体振动能量流动的可视化呈现。对比了触头正常接触和触指缺失缺陷下的GIS腔体振动能量流动特性的差异，结果表明，相比于接触正常状态，在触头接触缺陷状态下，总输入功率流100 Hz与200 Hz分量分别从6.64×10~(-6) W和4.81×10~(-6) W下降到6.35×10~(-6) W和4.55×10~(-6) W，使得外壳振动信号基频幅值比接触正常状态下减小6.25%。分析结果对于开展基于振动检测的GIS机械缺陷诊断提供了技术支持。
Keyword ：
功率流 功率流 振动 振动 气体绝缘设备 气体绝缘设备 结构声强法 结构声强法 触头 触头
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| GB/T 7714 | 王扬程 , 关向雨 , 陈志鹏 et al. 基于结构声强法的GIS机械振动传递特性 [J]. | 电工技术学报 , 2024 , 39 (16) : 5162-5171 . |
| MLA | 王扬程 et al. "基于结构声强法的GIS机械振动传递特性" . | 电工技术学报 39 . 16 (2024) : 5162-5171 . |
| APA | 王扬程 , 关向雨 , 陈志鹏 , 林建港 , 赵俊义 . 基于结构声强法的GIS机械振动传递特性 . | 电工技术学报 , 2024 , 39 (16) , 5162-5171 . |
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The fault characteristics of photovoltaic (PV) systems are greatly influenced by environmental factors, which causes grand challenges in PV fault detection. Therefore, this paper proposes an anomaly detection algorithm for grid-connected PV system via anomaly-transformer. Firstly, a PV platform was built to carry out fault experiments under different meteorological conditions, and a total of 218 sets of DC voltage/current datasets were constructed. Aiming at the characteristics of multi-dimensional time series data, the multi-branch anomaly-attention mechanism is used to calculate prior-association and series-association, then use transformer to reconstruct the loss values based on the obtained data. The association discrepancy is calculated as the index of anomaly detection, so as to achieve the goal of time-based localization of PV faults. The experimental results show that compared with graph deviation network (GDN), unsupervised anomaly detection (USAD) and other algorithms, the Precision of anomaly-transformer reaches 76.45% and 95.41% respectively in sunny and cloudy test data sets, and the F1-score reaches 86.65% and 97.65% respectively. It can accurately locate the fault time, which provides an effective method for PV fault detection. © Beijing Paike Culture Commu. Co., Ltd. 2024.
Keyword ：
Anomaly detection Anomaly detection Deep learning Deep learning Electric transformer testing Electric transformer testing Fault detection Fault detection Timing circuits Timing circuits
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| GB/T 7714 | Fu, Xiaoying , Jiang, Wujie , Zhang, Yanfeng et al. Fault Detection for Grid-Connected Photovoltaic System via Anomaly-Transformer Technique [C] . 2024 : 59-67 . |
| MLA | Fu, Xiaoying et al. "Fault Detection for Grid-Connected Photovoltaic System via Anomaly-Transformer Technique" . (2024) : 59-67 . |
| APA | Fu, Xiaoying , Jiang, Wujie , Zhang, Yanfeng , Xiong, Hengping , Guan, Xiangyu . Fault Detection for Grid-Connected Photovoltaic System via Anomaly-Transformer Technique . (2024) : 59-67 . |
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桥梁的风致涡激振动是影响随桥电缆疲劳寿命的因素之一，采用有限元法对随桥电缆在风致涡激振动作用下的疲劳寿命进行分析。首先，建立桥梁的二维瞬态流-固耦合数值模型，采用动网格和用户自定义函数(UDF)技术实现桥梁的瞬态振动位移响应求解；其次，将桥梁的单位振动位移作为边界条件施加在电缆上，获取电缆在单位振动位移作用下静力学应力求解结果；最后，通过求解的应力结果与一个周期的桥梁振动位移时程构建应力时间历程，并基于材料的S-N曲线和Miner线性累积损伤理论获取电缆的疲劳寿命。结果表明：在风致涡激振动作用下，作用在随桥电缆上的作用力近似为对称等幅循环载荷；电缆的疲劳损伤最大处出现在夹具固定附近的铝护套层上，寿命循环次数达3.3×10~5次，寿命约为206.25 h。该研究可为跨海电缆的安全可靠性设计和状态运行维护提供理论指导。
Keyword ：
有限元分析 有限元分析 流-固耦合 流-固耦合 疲劳寿命 疲劳寿命 随桥电缆 随桥电缆 风致涡激振动 风致涡激振动
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| GB/T 7714 | 陈志鹏 , 关向雨 , 蔡开明 et al. 风致涡激振动作用下随桥电缆的疲劳寿命分析 [J]. | 电线电缆 , 2023 , (04) : 52-57 . |
| MLA | 陈志鹏 et al. "风致涡激振动作用下随桥电缆的疲劳寿命分析" . | 电线电缆 04 (2023) : 52-57 . |
| APA | 陈志鹏 , 关向雨 , 蔡开明 , 刘江 , 林镕兴 , 王扬程 . 风致涡激振动作用下随桥电缆的疲劳寿命分析 . | 电线电缆 , 2023 , (04) , 52-57 . |
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对气体绝缘开关设备(gas insulated switchgear,GIS)典型部件的目标识别和温度提取是实现对设备发热状态红外智能检测的关键.文中提出一种基于混合域注意力机制(convolutional block attention module,CBAM)的改进YOLOv4算法,可实现对GIS母线、隔离开关等部件的快速目标检测和热点温度提取.首先,在某变电站现场采集原始红外图像,对图像进行锐化处理和部位标记,构建包含GIS典型部件的红外数据集.然后,利用深度可分离卷积网络降低模型参数量,并融入CBAM优化模型的识别能力,在此基础上构建基于改进YOLOv4的GIS红外部件目标快速检测算法.最后,采用灰阶差值方法对检测到的GIS典型目标部件进行热区温度值提取.结果表明,所提算法在GIS红外特征数据集上可以达到每秒31.5帧的识别速度和82.3％的识别准确率,明显优于其他目标算法,且GIS各部件的温升计算值与实测值误差在±1℃内.该算法可部署在无人机和巡检小车等边缘智能终端,实现对现场GIS设备温升状态的精细化识别和快速诊断,提升GIS设备健康状态管理数字化和智能化水平.
Keyword ：
YOLOv4 YOLOv4 气体绝缘开关设备(GIS) 气体绝缘开关设备(GIS) 混合域注意力机制(CBAM) 混合域注意力机制(CBAM) 温升提取 温升提取 红外图像 红外图像 轻量级网络 轻量级网络
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| GB/T 7714 | 刘江 , 关向雨 , 温跃泉 et al. 基于改进YOLOv4的GIS红外特征识别与温度提取方法 [J]. | 电力工程技术 , 2023 , 42 (1) : 162-168 . |
| MLA | 刘江 et al. "基于改进YOLOv4的GIS红外特征识别与温度提取方法" . | 电力工程技术 42 . 1 (2023) : 162-168 . |
| APA | 刘江 , 关向雨 , 温跃泉 , 吕朝伟 . 基于改进YOLOv4的GIS红外特征识别与温度提取方法 . | 电力工程技术 , 2023 , 42 (1) , 162-168 . |
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Target recognition and temperature extraction of the typical component of gas insulated switchgear (GIS) are the key to realizing the infrared intelligent detection of equipment heating state. In this paper, an improved YOLOv4 algorithm based on convolutional block attention module (CBAM) is proposed to achieve rapid target detection and hot spot temperature extraction of GIS bus, disconnector and other components. Firstly, the original infrared images are acquired at a substation site, and an infrared dataset containing typical GIS components is constructed by sharpening the images and marking components. Then, the deep separable convolutional network is used to reduce the amount of model parameters, and the CBAM is integrated to optimize the recognition ability of the model, on the basis of which a GIS infrared component target rapid detection algorithm with improved YOLOv4 is constructed. Finally, the gray-scale difference method is used to extract the temperature value of the hot area for the detected typical target components of GIS. The results show that the proposed algorithm can achieve a recognition speed of 31.5 frame per second and an recognition accuracy of 82.3% on the GIS infrared feature dataset, which is significantly better than other target algorithms. The error between the calculated value and the measured value of temperature rise of GIS components is within ±1℃. The algorithm proposed in this paper can be deployed in edge intelligent terminals such as unmanned aerial vehicles and inspection trolleys to achieve refined identification and rapid diagnosis of the temperature rise status of on-site GIS equipment, thus improving the digitalization and intelligence level of health management of GIS. © 2023, Editorial Department of Electric Power Engineering Technology. All rights reserved.
Keyword ：
Convolutional block attention module (CBAM) Convolutional block attention module (CBAM) Gas insulated switchgear (GIS) Gas insulated switchgear (GIS) Infrared image Infrared image Lightweight network Lightweight network Temperature rise extraction Temperature rise extraction YOLOv4 YOLOv4
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| GB/T 7714 | Liu, J. , Guan, X. , Wen, Y. et al. Infrared feature recognition and temperature extraction method of GIS components based on improved YOLOV4 [J]. | Electric Power Engineering Technology , 2023 , 42 (1) : 162-168 . |
| MLA | Liu, J. et al. "Infrared feature recognition and temperature extraction method of GIS components based on improved YOLOV4" . | Electric Power Engineering Technology 42 . 1 (2023) : 162-168 . |
| APA | Liu, J. , Guan, X. , Wen, Y. , Lyu, C. . Infrared feature recognition and temperature extraction method of GIS components based on improved YOLOV4 . | Electric Power Engineering Technology , 2023 , 42 (1) , 162-168 . |
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针对现场气体绝缘开关设备(gas insulated switchgear, GIS)振动检测结果易受外界背景噪声干扰的不足，文中提出基于生成对抗网络和卷积神经网络的现场GIS接触缺陷抗干扰检测框架。首先，开展GIS通流试验，获取在触指缺失、螺栓松动、存在分解物和导体对接深度不足4种典型缺陷下的振动波形，并收集包含背景噪声干扰的现场GIS振动波形作为参考，通过对振动数据进行图谱转化，构建用于背景噪声干扰去除和缺陷分类的数据集；其次，将现场振动图谱作为输入，采用周期一致生成对抗网络(cycle-consistent generative adversarial network, CycleGAN)对GIS进行现场背景噪声干扰去除；然后，采用AlexNet和ResNet18卷积网络结构对振动图谱特征进行提取；最后，采用全连接层对图谱特征进行分类，并对比不同振动信号图谱算法对分类结果的影响。结果表明，对于现场数据，所提模型的最大均值差异(maximum mean discrepancy, MMD)可达0.956 0,弗雷谢特起始距离(Fréchet inception distance, FID)可达62.09;Mel-ResNet18模型对GIS接触缺陷分类的准确率达99.43%。文中所提方法对于提高现场GIS振动检测和接触缺陷诊断结果的有效性具有重要应用价值。
Keyword ：
AlexNet AlexNet ResNet18 ResNet18 周期一致生成对抗网络(CycleGAN) 周期一致生成对抗网络(CycleGAN) 接触缺陷 接触缺陷 机械振动 机械振动 气体绝缘开关设备(GIS) 气体绝缘开关设备(GIS)
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| GB/T 7714 | 廖景雯 , 关向雨 , 林建港 et al. 基于CycleGAN和CNN的GIS振动信号去噪与机械缺陷识别 [J]. | 电力工程技术 , 2023 , 42 (05) : 37-45 . |
| MLA | 廖景雯 et al. "基于CycleGAN和CNN的GIS振动信号去噪与机械缺陷识别" . | 电力工程技术 42 . 05 (2023) : 37-45 . |
| APA | 廖景雯 , 关向雨 , 林建港 , 刘江 , 赵俊义 . 基于CycleGAN和CNN的GIS振动信号去噪与机械缺陷识别 . | 电力工程技术 , 2023 , 42 (05) , 37-45 . |
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