| 引用本文: | 唐治平,彭敏放,李光明,万勋,刘荣胜.基于重复脉冲法的变压器绕组匝间短路故障诊断[J].电力自动化设备,2018,(10): |
| TANG Zhiping,PENG Minfang,LI Guangming,WAN Xun,LIU Rongsheng.Diagnosis of inter-turn short circuit fault of transformer winding based on repetitive surge oscillograph[J].Electric Power Automation Equipment,2018,(10): |
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| 摘要: |
| 针对变压器绕组轻微匝间短路故障难以检测的问题，提出利用重复脉冲法的特征曲线进行匝间短路故障诊断的方法。分析了脉冲信号在变压器绕组内的传播过程，建立了反映该传播过程的变压器绕组分布参数电路模型，推导了发生匝间短路后波阻抗的变化规律。通过在变压器绕组一端输入一个低压脉冲，在另一端采集响应特性曲线，结合绕组匝间短路前后的2条响应特性曲线得到其特征曲线。分析特征曲线是否突起以此来判断是否发生匝间短路故障，从而实现故障诊断，而特征曲线的突起程度反映了绕组短路故障的严重程度。结合人工神经网络算法，对所提方法的故障识别率进行分析，仿真样本分析显示其故障识别率可达95%左右。仿真和实例分析结果表明了重复脉冲法对诊断变压器绕组匝间短路故障的可行性及准确性。 |
| 关键词: 变压器 匝间短路 重复脉冲法 特征曲线 人工神经网络 故障诊断 |
| DOI：10.16081/j.issn.1006-6047.2018.10.024 |
| 分类号:TM41 |
| 基金项目: |
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| Diagnosis of inter-turn short circuit fault of transformer winding based on repetitive surge oscillograph |
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TANG Zhiping1, PENG Minfang1, LI Guangming1, WAN Xun1,2, LIU Rongsheng1
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1.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;2.State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
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| Abstract: |
| Aiming at the problem that it is difficult to detect the slight inter-turn short circuit fault of transformer winding, a method of inter turn short circuit fault diagnosis based on the characteristic curve of RSO(Repetitive Surge Oscillograph) is proposed. The propagation process of the pulse signal in the transformer winding is analyzed, and the distribution parameter circuit model of the transformer winding reflecting the propagation process is established. The variation law of wave impedance after inter-turn short circuit is derived. By inputting a low-voltage pulse at one end of the transformer winding, the response characteristic curve can be collected at the other end, and the characteristic curve is obtained by combining the two response characteristic curves before and after the inter-turn short circuit. Whether the inter-turn short circuit fault occurs or not is determined by the characteristic curve is raised or not. The characteristic curve’s protrusion degree reflects the severity of the intern-turn short circuit fault. Combined with artificial neural network algorithm, the fault identification rate of the proposed method is analyzed, and the simulation sample analysis shows that fault identification rate of the proposed method is up to 95%. Simulation and case analysis show the feasibility and accuracy of RSO method for diagnosing transformer winding inter-turn short circuit fault. |
| Key words: power transformers inter-turn short circuit repetitive surge oscillograph characteristic curve artificial neural network fault diagnosis |
