电工技术学报  2023, Vol. 38 Issue (8): 2178-2190    DOI: 10.19595/j.cnki.1000-6753.tces.211990
电力系统与综合能源 |
基于时序卷积残差网络的主动配电系统线路短路故障诊断方案
褚旭1, 鲍泽宏1, 许立强2, 严亚兵2
1.湖南大学电气与信息工程学院 长沙 410082;
2.国网湖南省电力有限公司电力科学研究院 长沙 410007
Fault Line Diagnosis Scheme of Active Distribution System Based on Time-Sequence Convolution Residual Network
Chu Xu1, Bao Zehong1, Xu Liqiang2, Yan Yabing2
1. College of Electrical and Information Engineering Hunan University Changsha 410082 China;
2. Electric Power Research Institute of State Grid Hunan Electric Power Co. Ltd Changsha 410007 China
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摘要 主动配电系统结构复杂、控制灵活,线路短路故障特征微弱,亟须适应性强、精度高的诊断方法。该文提出一种基于时序卷积残差网络的主动配电系统线路短路故障诊断方案。该方案根据故障前后各特征通道之间在对应时刻的强相关性,利用时序卷积核沿时间轴方向卷积,提升感受野,减少信息损失,并利用残差网络的短接特性、深层挖掘故障特征,构建基于时序卷积(T-Conv)的残差网络模型。所提出的主动配电系统线路短路故障诊断方案融合故障检测、故障选型、故障定位功能,并将输出结果通过AND布尔算子结合,进一步提升故障诊断可信度。利用所搭建10 kV主动配电系统电磁暂态模型进行仿真验证,与现有同类型方法进行对比,结果表明所提故障诊断方案诊断精度高、计算速度快、无需附加信号处理算法,可直接端到端构建诊断模型。
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褚旭
鲍泽宏
许立强
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关键词 主动配电系统故障诊断时序卷积残差网络    
Abstract:The fault characteristics of active distribution system (ADS) are weak due to its complex structure and flexible control strategy, so the diagnosis method with strong adaptability and high precision is urgently needed. This paper examines a new fault diagnosis scheme for lines of active power distribution system founded on a time-sequence convolution residual network, which is used to detect, select and locate short circuit fault. According to the strong correlation between each feature channel signals measured at corresponding time of pre-fault and post-fault period, a residual network model based on time-sequence convolution (T-Conv) is built. For the proposed T-Conv residual network model, each time-sequence convolution kernel transverse telescopic width based on the number of adaptive features matching the input channel, ensure the receptive field lateral coverage to all the characteristics of the channel, to fully mix the channel between the features of information, enhance the receptive field, reducing the loss of information, and save the calculation process of convolution kernels lateral translation, save calculation time and improve calculation efficiency. The kernel size and step size are set according to the temporal morphological characteristics of fault transient and sampling frequency, and convolved downward along the time axis. The fault local features on the time axis are successively mined, mutation details are extracted from multiple directions, and complex, abstract and weak features of ADS fault signals are extracted efficiently. Furthermore, the short nature of residual network is used to generate a backoff mechanism to solve the problem of gradient disappearance in deep neural networks. Thus, each network layer can obtain enough gradients to update its parameters, which reduces the difficulty of model training. Fault selection and fault location belong to both classification tasks, and the front part of the network belongs to feature extraction. After mining corresponding features through training, only the corresponding number of neurons are needed to output the discriminant probability of each category according to the different task objectives. Therefore, this paper realizes fault selection and fault location functions by modifying the structure of the output layer of the model. In addition, the fault detection function can be realized and the reliability of fault diagnosis can be further improved by considering the normal category and combining with Boolean operator in the selection and location discrimination results. The fault diagnosis scheme results guide the staff to find abnormalities and repair faults in time, accelerate system recovery, and form an active fault diagnosis scheme for the distribution system that integrates fault detection, selection, and location functions. Finally, an electromagnetic transient model of 10 kV active distribution system is built using EMTDC. Experimental results show that for different transition resistance, fault location and fault initial Angle, this article proposed fault diagnosis scheme based on time-sequence convolution were diagnosed with high precision, fast computing speed, the advantages of without expert experience, avoid working condition of fault signal feature selection, time and frequency domain transformation, such as complicated process, direct "end-to-end" build a diagnosis model, implement exemption threshold diagnosis. Compared with other similar diagnosis methods based on recurrent neural network (RNN), artiflcal neutral networks (ANN) and convolutional neutral networks (CNN), which have single function and do not consider multi-function combination, this method effectively integrates and realizes the functions of fault detection, fault selection and fault location, and has advantages in diagnosis accuracy and calculation efficiency. In addition, as the proportion of training set samples decreases, the model still has high diagnostic accuracy, showing adaptability to different sample sizes and good generalization ability.
Key wordsActive distribution system    fault diagnosis    time-sequence convolution    residual network   
收稿日期: 2021-12-10     
PACS: TM75  
基金资助:国网湖南省电力有限公司科技项目(5216A522000N)、国网湖南省电力有限公司电力科学研究院资助项目(5216A5210036)和湖南省自然科学基金项目(2020JJ5056)资助
通讯作者: 鲍泽宏 男,1997年生,硕士,研究方向为基于特征及数据驱动的主动配电系统故障诊断方案。E-mail:baozehongyjs@hnu.edu.cn   
作者简介: 褚 旭 女,1988年生,博士,副教授,研究方向为交流/直流输配电系统故障诊断、控制与保护策略。E-mail:xu.chu@hnu.edu.cn
引用本文:   
褚旭, 鲍泽宏, 许立强, 严亚兵. 基于时序卷积残差网络的主动配电系统线路短路故障诊断方案[J]. 电工技术学报, 2023, 38(8): 2178-2190. Chu Xu, Bao Zehong, Xu Liqiang, Yan Yabing. Fault Line Diagnosis Scheme of Active Distribution System Based on Time-Sequence Convolution Residual Network. Transactions of China Electrotechnical Society, 2023, 38(8): 2178-2190.
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