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学者姓名：杨耿杰
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Abstract ：
考虑到传统的基于电磁辐射(electromagnetic radiation,EMR)信号的光伏阵列电弧故障定位方法存在采样条件严苛、定位精度低等问题,提出一种基于网格指纹匹配的电弧故障定位新方法.首先,使用低采样率获取电弧EMR信号,并提取其均方根值作为代表EMR强度的特征指标.然后,利用BP神经网络(back propagation neural network,BPNN)挖掘辐照度、信号接收距离与电弧EMR信号强度的内在联系,建立预测模型.接着,根据BPNN输出的双天线阵列与电弧间的预测距离,利用三角定位法初步求得电弧所在区域.最后,网格化划分电弧所在区域的光伏组件,生成网格指纹信息,并将预测距离与指纹信息最匹配的网格的中心坐标作为电弧发生位置的最终预测坐标.实验结果表明,所提算法具备良好的定位能力与适应性,对电弧故障定位的平均绝对误差为0.306 m,在定位精度与经济性上均优于EMR衰减模型定位法.
Keyword ：
BP神经网络 BP神经网络 光伏阵列 光伏阵列 电弧故障定位 电弧故障定位 电磁辐射 电磁辐射 网格指纹匹配 网格指纹匹配
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| GB/T 7714 | 金辉 , 高伟 , 林亮世 et al. 基于网格指纹匹配的光伏阵列电弧故障定位方法 [J]. | 高电压技术 , 2024 , 50 (2) : 834-845 . |
| MLA | 金辉 et al. "基于网格指纹匹配的光伏阵列电弧故障定位方法" . | 高电压技术 50 . 2 (2024) : 834-845 . |
| APA | 金辉 , 高伟 , 林亮世 , 杨耿杰 . 基于网格指纹匹配的光伏阵列电弧故障定位方法 . | 高电压技术 , 2024 , 50 (2) , 834-845 . |
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The problems of strict sampling conditions and low positioning accuracy exist in the traditional photovoltaic array arc fault location method based on electromagnetic radiation (EMR) signal, accordingly, we propose a new arc fault location method based on grid fingerprint matching. Firstly, the EMR signal of the arc is acquired with a low sampling rate, and its root mean square value is extracted as the characteristic index representing the EMR intensity. Then, BP neural network (BPNN) is adopted to mine the internal relationship among irradiance, signal receiving distance and arc EMR signal intensity, and a prediction model is established. Subsequently, according to the predicted distance between the dual-antenna array output by BPNN and the arc, the area where the arc is located is preliminarily acquired by using the triangulation method. Finally, the photovoltaic module in the located area is divided into grids to generate grid fingerprint information, and the center coordinate of the grid that most matches the predicted distance and fingerprint information is taken as the final predicted coordinate of the arc occurrence position. The experiment results show that the proposed algorithm has good positioning ability and adaptability, and the average absolute error of arc fault location is 0.306 m, which is superior to the EMR attenuation model positioning method in positioning accuracy and economy. © 2024 Science Press. All rights reserved.
Keyword ：
Antenna arrays Antenna arrays Backpropagation Backpropagation Electromagnetic wave emission Electromagnetic wave emission Location Location Neural networks Neural networks Pattern matching Pattern matching
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| GB/T 7714 | Jin, Hui , Gao, Wei , Lin, Liangshi et al. Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching [J]. | High Voltage Engineering , 2024 , 50 (2) : 805-815 . |
| MLA | Jin, Hui et al. "Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching" . | High Voltage Engineering 50 . 2 (2024) : 805-815 . |
| APA | Jin, Hui , Gao, Wei , Lin, Liangshi , Yang, Gengjie . Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching . | High Voltage Engineering , 2024 , 50 (2) , 805-815 . |
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To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation, a control method involving flexible multistate switches (FMSs) is proposed in this study. This approach is based on an improved double-loop recursive fuzzy neural network (DRFNN) sliding mode, which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults. First, an improved DRFNN sliding mode control (SMC) method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system. To improve the robustness of the system, an adaptive parameter-adjustment strategy for the DRFNN is designed, where its dynamic mapping capabilities are leveraged to improve the transient compensation control. Additionally, a quasi-continuous second- order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability. The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem. A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink. The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis. © 2024
Keyword ：
Adaptive control systems Adaptive control systems Electric arcs Electric arcs Electric grounding Electric grounding Electric power distribution Electric power distribution Fuzzy inference Fuzzy inference Fuzzy neural networks Fuzzy neural networks MATLAB MATLAB Sliding mode control Sliding mode control System stability System stability
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| GB/T 7714 | Liao, Jianghua , Gao, Wei , Yang, Yan et al. Control method based on DRFNN sliding mode for multifunctional flexible multistate switch [J]. | Global Energy Interconnection , 2024 , 7 (2) : 190-205 . |
| MLA | Liao, Jianghua et al. "Control method based on DRFNN sliding mode for multifunctional flexible multistate switch" . | Global Energy Interconnection 7 . 2 (2024) : 190-205 . |
| APA | Liao, Jianghua , Gao, Wei , Yang, Yan , Yang, Gengjie . Control method based on DRFNN sliding mode for multifunctional flexible multistate switch . | Global Energy Interconnection , 2024 , 7 (2) , 190-205 . |
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DC arc faults are major causes of electrical fires in photovoltaic (PV) systems. During the operation and maintenance of these systems, it is essential not only to identify arc faults but also to determine their exact locations accurately. To address the issue of DC arc fault localization in PV systems, this study investigates the electromagnetic radiation (EMR) characteristics of fault arcs and proposes a method for DC arc fault localization using the redundant antenna array and the ellipse algorithm. Firstly, during arc combustion, the EMR signals collected by antennas are subjected to median filtering to calculate the root mean square (RMS), which serves as the signal strength. An artificial neural network (ANN) model is constructed, which uses the signal strength and irradiance to predict the distance between the fault point and the receiving point. Subsequently, various redundant antenna array configurations are evaluated to assess the impact of different antenna quantities and layouts on localization accuracy. Once the optimal layout is determined, the three antennas with the strongest signal are selected. Their coordinates, along with the predicted distances to the fault point, are input into the ellipse algorithm, which is improved by trilateration, to obtain the locations of arc faults. Finally, the density-based spatial clustering of applications with noise (DBSCAN) method is used to fuse multiple measurement results, eliminate interference, and confirm the final fault coordinates. Experimental results demonstrate that the proposed location method exhibits excellent positioning capability and adaptability, with an average positioning error of 0.365 m.
Keyword ：
Arc fault location Arc fault location DBSCAN DBSCAN Ellipse algorithm Ellipse algorithm Photovoltaic systems Photovoltaic systems Redundant antenna array Redundant antenna array
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| GB/T 7714 | Lin, Liangshi , Gao, Wei , Yang, Gengjie . A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm [J]. | SOLAR ENERGY , 2024 , 274 . |
| MLA | Lin, Liangshi et al. "A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm" . | SOLAR ENERGY 274 (2024) . |
| APA | Lin, Liangshi , Gao, Wei , Yang, Gengjie . A DC arc fault location method for PV systems based on redundant antenna array and ellipse algorithm . | SOLAR ENERGY , 2024 , 274 . |
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The complexity and uncertainty of vibration signals from distribution transformers pose significant challenges for diagnosing mechanical faults. To address this, this paper proposes a novel fault diagnosis model for distribution transformers, which combines a cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) with an open-set domain adaptation classifier (OSDA-C). Specifically, in order to extract more comprehensive features, a convolutional autoencoder (CAE) model based on multi-output objectives is constructed to extract the timefrequency domain characteristics of transformer vibration signals. Multiple-scale convolutional layers are incorporated into the convolutional autoencoder to enable multi-range feature extraction. Additionally, parameter optimization is achieved using the crayfish optimization algorithm (COA). Subsequently, an open-set domain adaptation module is integrated into the convolutional neural network classifier to establish boundaries for each category and facilitate the identification of transformer fault categories, including unknown-type faults. The experimental results demonstrate that the proposed method is effective for fault identification in both drytype and oil-immersed transformers, with average accuracy reaching 99.35% and 99.62%, respectively. For unknown-type faults, the accuracy also achieved 100% and 97.5%, respectively.
Keyword ：
Cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) Cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) Distribution transformer Distribution transformer Mechanical faults Mechanical faults Open-set domain adaptation classifier(OSDA-C) Open-set domain adaptation classifier(OSDA-C) Vibration signals Vibration signals
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| GB/T 7714 | Huang, Haiyan , Gao, Wei , Yang, Gengjie . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults [J]. | MEASUREMENT , 2024 , 238 . |
| MLA | Huang, Haiyan et al. "Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults" . | MEASUREMENT 238 (2024) . |
| APA | Huang, Haiyan , Gao, Wei , Yang, Gengjie . Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults . | MEASUREMENT , 2024 , 238 . |
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In general, residual current devices (RCDs) have problems such as protection dead-zone and difficulty in threshold setting. A new method for identification of biological electric shock (BES) in low-voltage distribution network based on threshold method is proposed. Firstly, the total residual current of the circuit is denoised by Kalman filter, and then two threshold methods are investigated to determine the electric shock (ES) event and type respectively. Specifically, the first threshold consists of the maximum and average value of the current changes in the previous period, which is an adaptive value of dynamic change. If the current sampling value exceeds the threshold for 10 times in total within 5 ms, an ES is considered to have occurred. Then considering that the amplitude of the waveform of the first three periods after BES has the characteristics of gradual changes, the sampling values of the three periods are recorded. The second threshold is a fixed threshold which is obtained by weighting the phase point changes corresponding to the second-period and the third-period waveforms, and then the specific ES types are distinguished. The proposed method is implemented on hardware devices and analyzed in various common ES situations. The results show that for the three cycles of waveforms collected after the occurrence of grounding or ES, the accuracy of this method is 97.84% and the recognition time is 2.07 ms. In addition, based on the analysis of the actual BES data, a simple digital model is proposed to simulate the actual biological response, and it can be of great help in the subsequent study of such problems.
Keyword ：
adaptive startup threshold adaptive startup threshold biological electric shock(BES) biological electric shock(BES) Covariance matrices Covariance matrices Distribution networks Distribution networks Electric shock Electric shock Fibrillation Fibrillation Kalman filters Kalman filters Low voltage Low voltage Power system reliability Power system reliability Residual current device(RCD) Residual current device(RCD) weighted sum of deviation weighted sum of deviation
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| GB/T 7714 | Yang, Gengjie , Quan, Shengxin , Gao, Wei . A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network [J]. | IEEE TRANSACTIONS ON POWER DELIVERY , 2023 , 38 (3) : 1558-1568 . |
| MLA | Yang, Gengjie et al. "A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network" . | IEEE TRANSACTIONS ON POWER DELIVERY 38 . 3 (2023) : 1558-1568 . |
| APA | Yang, Gengjie , Quan, Shengxin , Gao, Wei . A New-Designed Biological Electric Shock Identification Method in Low-Voltage Distribution Network . | IEEE TRANSACTIONS ON POWER DELIVERY , 2023 , 38 (3) , 1558-1568 . |
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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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由于串联电弧故障特征表现不足以及样本不平衡的问题,导致传统的诊断算法检测效果不佳.提出了一种基于图像识别的光伏阵列串联电弧故障诊断方法:利用格拉姆角和场(GASF)将发生串联电弧故障时的暂态电流数据编码为二维图像,从而放大电弧故障的本质特征;深度卷积生成对抗网络(DCGAN)被用来增扩电弧故障GASF特征图像,以均衡正常与故障样本数量;训练一个LeNet-5诊断模型完成电弧故障的识别.经过实验验证,所提方法有效提升了光伏阵列串联电弧故障的辨识度,且具备优秀的抗干扰能力,对实测数据的整体识别准确率高达99.5％.
Keyword ：
串联电弧故障 串联电弧故障 光伏阵列 光伏阵列 格拉姆角和场 格拉姆角和场 深度卷积生成对抗网络 深度卷积生成对抗网络
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| GB/T 7714 | 金辉 , 高伟 , 杨耿杰 . 一种光伏阵列串联电弧故障智能检测方法 [J]. | 电工电气 , 2023 , (1) : 43-47,66 . |
| MLA | 金辉 et al. "一种光伏阵列串联电弧故障智能检测方法" . | 电工电气 1 (2023) : 43-47,66 . |
| APA | 金辉 , 高伟 , 杨耿杰 . 一种光伏阵列串联电弧故障智能检测方法 . | 电工电气 , 2023 , (1) , 43-47,66 . |
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In recent years, the methods of machine learning are widely investigated to resolve the series arc fault (SAF) diagnosis problem in photovoltaic (PV) arrays. However, owing to the factors such as weak signal characteristics, long algorithm execution time, and sample imbalance in practical applications, these methods may have diffi-culties of detecting the SAF. To address these problems, a method based on the Gramian angular summation field (GASF) combined with the squeeze and excitation-deep convolution generative adversarial network (SE-DCGAN) is proposed. Firstly, the absolute difference of margin factor (ADMF) of the current signal is calculated to accurately extract the transient current data when the SAF occurs. Thereafter, the GASF is used to convert transient current data into two-dimensional images to amplify the universal characteristics of the SAF. Subse-quently, the SE-DCGAN is adopted to augment the GASF images of the SAF to solve the problem of limited SAF samples. Finally, a convolutional neural network (CNN) is trained to identify the SAF. Also, a fusion sample training method is proposed in this research, that is, normal samples of different PV systems are added to the training set to enhance the generalization ability of CNN. The advantages of the proposed method are that the identification of SAF is improved by converting one-dimensional signals into two-dimensional images, and the generalization ability of the detection model is improved by exploiting the common features of SAFs and fusion training. The validity and generalization ability of the proposed method are verified by three datasets under different PV systems. Experimental results reveal that the proposed method can achieve high recognition ac-curacy for the measured data; moreover, no misjudgments occurred in identifying the interference events such as maximum power point tracking (MPPT) adjustment and irradiance mutation (IM). In addition, the experiments confirm that the fusion training method enables the model more universal and applicable.
Keyword ：
generative adversarial network (SE-DCGAN) generative adversarial network (SE-DCGAN) Gramian angular summation field (GASF) Gramian angular summation field (GASF) Improvement of generalization ability Improvement of generalization ability Photovoltaic (PV) system Photovoltaic (PV) system Series arc fault (SAF) Series arc fault (SAF) Squeeze and excitation -deep convolution Squeeze and excitation -deep convolution
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| GB/T 7714 | Gao, Wei , Jin, Hui , Yang, Gengjie . Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN [J]. | ADVANCED ENGINEERING INFORMATICS , 2022 , 54 . |
| MLA | Gao, Wei et al. "Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN" . | ADVANCED ENGINEERING INFORMATICS 54 (2022) . |
| APA | Gao, Wei , Jin, Hui , Yang, Gengjie . Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN . | ADVANCED ENGINEERING INFORMATICS , 2022 , 54 . |
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