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学者姓名：关向雨
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Abstract ：
同步发电机运行过程中可能出现诸如转子偏心、匝间短路和静电荷等缺陷，危及电机的安全运行。在对不同缺陷下轴电压信号和机械振动信号非线性相关分析基础上，提出融合轴电压-振动特征和深度学习的同步发电机缺陷诊断方法。首先，搭建三相同步发电机缺陷物理模拟试验平台，获取不同工况和缺陷下轴电压信号和机械振动信号数据，采用核典型相关分析获取了轴电压信号和振动信号的相关系数；采用梅尔语谱进行轴电压和振动信号图谱预处理，采用并行双分支残差网络分别对轴电压和振动图谱的高维特征进行提取，并采用双线性池化算法对不同模态的高维特征进行融合，在此基础上构建了融合轴电压-振动特征的同步发电机缺陷分类模型。结果表明：轴电压信号和同步电机本体振动信号关联度在故障和正常情况下均超过0.9,所提出的轴电压-振动联合诊断模型在测试集上的准确度、漏报率和误报率等性能方面优于单一轴电压和单一振动诊断算法。本文工作旨在通过监测和分析发电机的运行状态，及时识别潜在故障，提高发电机的运行可靠性。
Keyword ：
信息融合 信息融合 并行双分支残差网络 并行双分支残差网络 故障诊断 故障诊断 机械振动 机械振动 相关分析 相关分析 轴电压 轴电压
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| GB/T 7714 | 张杭 , 关向雨 , 廖景雯 et al. 融合轴电压-振动特征的同步电机缺陷诊断 [J]. | 电机与控制学报 , 2025 , 29 (07) : 53-62 . |
| MLA | 张杭 et al. "融合轴电压-振动特征的同步电机缺陷诊断" . | 电机与控制学报 29 . 07 (2025) : 53-62 . |
| APA | 张杭 , 关向雨 , 廖景雯 , 徐欣灵 , 陈晓坤 . 融合轴电压-振动特征的同步电机缺陷诊断 . | 电机与控制学报 , 2025 , 29 (07) , 53-62 . |
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为实现不同电工材料表面机械振动的非接触测量,搭建了全光纤 1 550 nm激光多普勒测振(LDV)系统,针对不同电工材料表面粗糙度对反射光的影响以及 IQ 信号不平衡因子对解调波形的影响,提出一种融合材料表面粗糙度光学补偿和正交解调补偿的综合补偿算法.首先基于射线光学分析不同电工材料表面粗糙度对收发一体光学镜头耦合效率的影响,提出采用平凹-凹凹-平凸透镜方案的光学天线前端补偿算法.分析了 IQ信号幅相不平衡对解调结果的影响,通过镜像抑制算法消除该影响,建立不平衡模型验证补偿算法的有效性.搭建了不同电工材料标定平台和气体绝缘开关(GIS)设备振动平台,对所提出的光学和信号补偿算法进行验证.结果表明,该文所提光学补偿方法使不同电工材料表面的激光平均耦合效率提高了 21.92%,信号补偿前的解调信号存在 25.04 dB 的镜像干扰比率(IIR),经过 IQ 信号补偿后,信号的信噪比提高了25.8 dB.验证了所研发的系统能够应用于不同电工设备表面 3～10 m 距离和 10 Hz～2 kHz 频率范围机械振动的无损带电检测.
Keyword ：
光学天线 光学天线 射线光学仿真 射线光学仿真 正交解调补偿 正交解调补偿 激光多普勒测振 激光多普勒测振
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| GB/T 7714 | 赖泽楷 , 关向雨 , 涂嘉毅 et al. 基于激光多普勒测振的电力设备表面振动测量及补偿算法 [J]. | 电工技术学报 , 2025 , 40 (6) : 1707-1717 . |
| MLA | 赖泽楷 et al. "基于激光多普勒测振的电力设备表面振动测量及补偿算法" . | 电工技术学报 40 . 6 (2025) : 1707-1717 . |
| APA | 赖泽楷 , 关向雨 , 涂嘉毅 , 林建港 , 徐欣灵 . 基于激光多普勒测振的电力设备表面振动测量及补偿算法 . | 电工技术学报 , 2025 , 40 (6) , 1707-1717 . |
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Compared with a traditional distribution transformer with silicon steel sheet as the core material, the no-load loss of an amorphous alloy transformer is greatly reduced due to its core using iron-based amorphous metal material, which has been applied in many countries. However, due to the brittleness of its amorphous strip, an amorphous alloy transformer is prone to debris in the process of production, transportation and work. The charge and migration characteristics of these debris will reduce the insulation strength of the transformer oil and endanger the safe operation of the transformer. In this paper, a charge measurement platform of amorphous alloy debris is set up, and the charging characteristics of amorphous alloy core debris under different flow velocities, particle radius and plate electric field strength are obtained. The results show that with an increase in pipeline flow velocity, the charge-to-mass ratio of the debris increases first and then decreases. With an increase in electric field strength, the charge-to-mass ratio of the debris increases; with an increase in the number of debris, the charge-to-mass ratio of the debris decreases; with an increase in debris size, the charge-to-mass ratio of the debris increases. The debris with different charge-to-mass ratios and types obtained from the above experiments are added to the simulation model of an amorphous alloy transformer. The lattice Boltzmann method (LBM) coupled with the discrete element method (DEM) is used to simulate the migration process of metal particles in an amorphous alloy transformer under the combined action of gravity, buoyancy, electric field force and oil flow resistance under electrothermal excitation boundary. The results show that the trajectory of the debris is related to the initial position, electric field strength and oil flow velocity. The LBM-DEM calculation model and charge measurement platform proposed in this paper can provide a reference for studying the charge mechanism and migration characteristics of amorphous alloy core debris in insulating oil.
Keyword ：
amorphous alloy core debris amorphous alloy core debris charge characteristics charge characteristics discrete element method discrete element method lattice Boltzmann method lattice Boltzmann method migration characteristic migration characteristic
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| GB/T 7714 | Yu, Wenxu , Guan, Xiangyu . Study on the Charge Characteristics and Migration Characteristics of Amorphous Alloy Core Debris [J]. | MATERIALS , 2025 , 18 (18) . |
| MLA | Yu, Wenxu et al. "Study on the Charge Characteristics and Migration Characteristics of Amorphous Alloy Core Debris" . | MATERIALS 18 . 18 (2025) . |
| APA | Yu, Wenxu , Guan, Xiangyu . Study on the Charge Characteristics and Migration Characteristics of Amorphous Alloy Core Debris . | MATERIALS , 2025 , 18 (18) . |
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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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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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Abstract ：
振动测量对发现气体绝缘开关设备(gas insulated switchgear，GIS)潜在性缺陷具有重要意义，但GIS本体振动信号易受基础振动、测量噪声以及环境噪声的影响，使得现场GIS振动带电检测和机械缺陷诊断的效果较差。针对此问题，提出一种基于奇异值分解(singular value decomposition, SVD)-改进自适应啁啾模态分解(improve adaptive chirp mode decomposition, IACMD)的现场振动信号降噪算法。该方法首先利用SVD对原始振动信号进行预处理，滤除低频基础振动和测量噪声，其次利用鱼鹰优化算法(osprey optimization algorithm, OOA)对处理后的信号进行自适应模态分解，得到分解后的固有模态(intrinsic mode functions，IMF)分量，再利用互相关系数筛选有效分量重构振动信号。模拟信号与现场信号测试结果表明：与OOA-自适应啁啾模态分解(adaptive chirp mode decomposition，ACMD)和SVD-变分模态分解(variational mode decomposition, VMD)相比，所提出的SVD-IACMD算法可以去除基础振动、测量噪声和环境噪声，保留GIS本体振动的基频和谐波分量，为GIS现场抗干扰振动检测和机械缺陷诊断提供技术支持。
Keyword ：
信号降噪 信号降噪 奇异值分解(SVD) 奇异值分解(SVD) 改进自适应啁啾模态分解(IACMD) 改进自适应啁啾模态分解(IACMD) 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) 鱼鹰优化算法(OOA) 鱼鹰优化算法(OOA)
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| GB/T 7714 | 涂嘉毅 , 关向雨 , 赵俊义 et al. 基于SVD-IACMD的GIS振动信号去噪算法 [J]. | 电力工程技术 , 2024 , 43 (6) : 163-172 . |
| MLA | 涂嘉毅 et al. "基于SVD-IACMD的GIS振动信号去噪算法" . | 电力工程技术 43 . 6 (2024) : 163-172 . |
| APA | 涂嘉毅 , 关向雨 , 赵俊义 , 林建港 , 赖泽楷 . 基于SVD-IACMD的GIS振动信号去噪算法 . | 电力工程技术 , 2024 , 43 (6) , 163-172 . |
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Abstract ：
梅花触头接触不良而引发的过热性故障是开关柜和气体绝缘组合电器的典型缺陷之一，研究不同气体介质中梅花触头的载流摩擦特性和剩余寿命预测对于准确评估该类型设备的健康状态具有重要意义。文中根据真实梅花触头材料和工艺加工了试验试样，采用自行搭建的载流摩擦台开展空气、N2和SF6气体介质下的载流摩擦试验并获取接触电阻的退化曲线。分析触头表面形貌得知：试样表面在大电流和机械位移作用下出现了明显的粘着磨损，气体介质通过改变磨损区域的形貌和元素组成影响触头的退化过程。根据触头的 V- T关系确定了接触电阻失效阈值，利用Savitzky-Golay滤波器对接触电阻原始曲线进行平滑滤波，采用贝叶斯更新的Wiener退化模型对不同气体介质下的梅花触头进行寿命预测，结果表明：与常规Wiener模型相比，采用贝叶斯更新的Wiener退化模型具有较好的预测准确度。空气中梅花触头的剩余寿命为9 500周期，N2中梅花触头的剩余寿命为61 300周期，SF6中梅花触头的剩余寿命为32 200周期。
Keyword ：
失效预测 失效预测 梅花触头 梅花触头 气体介质 气体介质 表面形貌 表面形貌 载流摩擦 载流摩擦 退化曲线 退化曲线
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| GB/T 7714 | 林镕兴 , 王扬程 , 陈志鹏 et al. 气体介质影响下梅花触头载流磨损特性与电接触寿命预测 [J]. | 电力工程技术 , 2023 , 42 (6) : 197-205 . |
| MLA | 林镕兴 et al. "气体介质影响下梅花触头载流磨损特性与电接触寿命预测" . | 电力工程技术 42 . 6 (2023) : 197-205 . |
| APA | 林镕兴 , 王扬程 , 陈志鹏 , 关向雨 , 刘江 , 蔡开明 . 气体介质影响下梅花触头载流磨损特性与电接触寿命预测 . | 电力工程技术 , 2023 , 42 (6) , 197-205 . |
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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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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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