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学者姓名：关向雨
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Abstract ：
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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现场变负荷运行条件下的油浸式变压器动态载荷（DTR）和热寿命损失与设备的瞬态温升密切相关。该文提出了一种耦合电网络的格子玻耳兹曼（LBM）物理在环仿真模型，实现对电网络约束下的DTR实时评估。采用D2Q9模型对流体流动和热格子玻耳兹曼方程（LBEs）进行求解捕获变压器内部瞬态的油流和温升过程；在Simulink环境中采用等价电流源模型构建多级负载的电网络约束，并将建立的变压器LBM模型作为元件进行数值封装完成物理在环的仿真模型构建。与有限体积法（FVM）相比，所建立的模型热点温升的稳态和瞬态求解结果的误差分别为2.60%和6.44%，热点温升变化趋势与变压器负载导则吻合，验证了所提方法的有效性。基于该模型对恒定25℃、夏季和冬季典型环境温度变化下的油浸式变压器负载能力进行了评估，结果显示在相对绝缘寿命损失小于1的前提下，环境温度恒定25℃、夏季典型变化温度和冬季典型变化温度下最大的负载系数分别为1.20、1.10和1.60。所提出的基于LBM的仿真模型对油浸式变压器温升的实时监测、负载能力评估和动态增容提供了一种有效方法。
Keyword ：
动态变压器载荷 动态变压器载荷 格子玻耳兹曼方法 格子玻耳兹曼方法 油浸式变压器 油浸式变压器 相对绝缘寿命 相对绝缘寿命
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| GB/T 7714 | 于文旭 , 关向雨 , 赵俊义 et al. 基于格子玻耳兹曼的油浸式变压器瞬态温升模拟与负载能力评估 [J]. | 电工技术学报 , 2025 , 40 (10) : 3315-3325 . |
| MLA | 于文旭 et al. "基于格子玻耳兹曼的油浸式变压器瞬态温升模拟与负载能力评估" . | 电工技术学报 40 . 10 (2025) : 3315-3325 . |
| APA | 于文旭 , 关向雨 , 赵俊义 , 涂嘉毅 , 赖泽楷 . 基于格子玻耳兹曼的油浸式变压器瞬态温升模拟与负载能力评估 . | 电工技术学报 , 2025 , 40 (10) , 3315-3325 . |
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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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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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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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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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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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In this work, a 1550-nm full-fiber Doppler lidar via software-defined platform is built to realize flexible and low-cost contactless vibration measurement of high-voltage power equipment. A 1550-nm fiber layout is designed to generate optical interference between vibration signal and carrier wave. The reflected vibration signal is collected by an optical transceiver and the carrier wave is generated by an acousto-optic modulator (AOM). The optical beat signal is collected by a balanced detector (BD) then sent into a general software defined radio (SDR) receiver. By GNU developing platform, the target mechanical vibration signal is demodulated and several flexible functions such as speed-acceleration trans, harmonic component analysis and fault diagnosis is realized. Performance of Doppler lidar is first verified on mechanical vibration source by PZT vibration actuator, results show that the designed lidar could retrieve 50 Hz-20 kHz mechanical vibration signals within the working distance is up to 20 m. Further case application scenarios on the power transformer and gas-insulated switchgear (GIS) are also conducted to verify the feasibility of proposed lidar.
Keyword ：
Doppler Doppler lidar lidar power equipment power equipment software-defined software-defined vibration detection vibration detection
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| GB/T 7714 | Guan, Xiangyu , Lv, ChaoWei , Zheng, Guanming et al. Software-Defined 1550-nm Full-Fiber Doppler Lidar for Contactless Vibration Measurement of High Voltage Power Equipment [J]. | RADIOENGINEERING , 2023 , 32 (3) : 391-399 . |
| MLA | Guan, Xiangyu et al. "Software-Defined 1550-nm Full-Fiber Doppler Lidar for Contactless Vibration Measurement of High Voltage Power Equipment" . | RADIOENGINEERING 32 . 3 (2023) : 391-399 . |
| APA | Guan, Xiangyu , Lv, ChaoWei , Zheng, Guanming , Pan, Zhuohong , Cai, Kaiming . Software-Defined 1550-nm Full-Fiber Doppler Lidar for Contactless Vibration Measurement of High Voltage Power Equipment . | RADIOENGINEERING , 2023 , 32 (3) , 391-399 . |
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This article presents a contact fault diagnosis method of gas-insulated switchgear (GIS) plug-in connector via magnetic field measurement, magnetic field visualization, and deep neural network (DNN) classifiers. First, the surroundingmagnetic field of GIS plug-in connector with normal contact (NC) condition and with artificially designed contact failures was measured by the Hall sensor array. Then, the measured magnetic field was gathered with an original matrix of 16 x 16 dimensions. The |original matrix was then visualized by the max-min normalization and correlation matrix. Database containing 11 000 magnetic field images was labeled and segmented as training, validation, and test datasets. Furthermore, high-dimensional features of input magnetic field images were extracted by different DNN filters, including convolutional neural network (CNN), simple recurrent neural network (Sim-RNN), and long short-term memory (LSTM) network. Then, extracted high-dimensional features were fed into a fully connected (Fc) neural network with SoftMax classifiers to identify different contact faults. Finally, the performance of different DNN-based classifiers is compared by the fault classification merits, t-distributed stochastic neighbor embedding (t-SNE) feature clustering, and confusion matrixes. Results show that the DNN-based model could achieve contact fault classification task with an accuracy of 97.7% and F-1_score of 0.985. Therefore, the proposed method is useful for designing a high-performance contact status monitoring system of GIS equipment, thus improving its operation safety.
Keyword ：
Contact fault Contact fault deep neural network (DNN) deep neural network (DNN) gas-insulated switchgear (GIS) gas-insulated switchgear (GIS) magnetic field measurement magnetic field measurement plug-in connector plug-in connector
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| GB/T 7714 | Guan, Guan , Xue, Shupeng , Peng, Hui et al. Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers [J]. | IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING , 2022 , 45 (3) : 262-271 . |
| MLA | Guan, Guan et al. "Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers" . | IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 45 . 3 (2022) : 262-271 . |
| APA | Guan, Guan , Xue, Shupeng , Peng, Hui , Shu, Naiqiu , Gao, Wei , Gao, David Wenzhong . Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers . | IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING , 2022 , 45 (3) , 262-271 . |
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There is a limitation in the process of acoustic signal detection, which lies in serious background noise interference and the complex correlation between acoustic signal characteristics and operation states. Integrating the denoising model and feature classification model, a method of transformer acoustic signal anti-interference detection and operation state detection based on deep learning is proposed in this paper. Through tests in anechoic rooms, acoustic signals of transformers in the normal state or under harmonic load are acquired. Combining these signals with the background noise, a dataset containing 12000 samples of acoustic signals is constructed. To implement anti-interference detection, Conv-TasNet is utilized to get the transformer acoustic signal and environmental noise separated; then, ResNet is utilized to classify the operation states of the transformer accurately. Results show that compared with the blind source separation method through RNN and FastICA, the denoising model established in this paper improves Si-SDRi parameters by 37.4dB and 17.53dB respectively, and the transformer operation state classification model established in this paper classifies the test dataset with an accuracy of 97.7%, thus providing an effective method for the extraction of transformer acoustic signal and diagnosis of transformer operation states in complex environments. © Published under licence by IOP Publishing Ltd.
Keyword ：
Acoustic noise Acoustic noise Blind source separation Blind source separation Classification (of information) Classification (of information) Deep learning Deep learning Signal detection Signal detection Signal to noise ratio Signal to noise ratio Statistical tests Statistical tests
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| GB/T 7714 | Lin, Yuyin , Lin, Yuzi , Wu, Qingyu et al. Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet [C] . 2022 . |
| MLA | Lin, Yuyin et al. "Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet" . (2022) . |
| APA | Lin, Yuyin , Lin, Yuzi , Wu, Qingyu , Wu, Xinhai , Tu, Jiayi , Ren, Weisheng et al. Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet . (2022) . |
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