| 基于信息融合和CS-SVM的变压器绕组变形故障诊断方法研究 |
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| 引用本文： | 甘锡淞,李云,傅成华,郭辉,杨亭榆.基于信息融合和CS-SVM的变压器绕组变形故障诊断方法研究[J].电力系统保护与控制,2018,46(1):156-161. |
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| 作者姓名： | 甘锡淞 李云 傅成华 郭辉 杨亭榆 |
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| 作者单位： | 四川理工学院自动化与信息工程学院,四川 自贡 643000,四川工商职业技术学院,四川 成都 611830,四川理工学院自动化与信息工程学院,四川 自贡 643000,四川理工学院自动化与信息工程学院,四川 自贡 643000,四川理工学院自动化与信息工程学院,四川 自贡 643000 |
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| 基金项目： | 四川省科技厅支撑项目(2013GZ0030);四川理工学院研究生创新基金资助项目(y2016033) |
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| 摘 要： | 变压器绕组在遭受短路故障后易产生变形，传统的频率响应分析或短路阻抗分析在绕组变形检测过程中具有一定的片面性。提出一种基于信息融合和CS-SVM(布谷鸟优化的支持向量机)的变压器绕组变形故障诊断方法，通过将绕组变形相关的检测数据融合成SVM的输入样本，并放入根据人工经验训练好的CS-SVM来进行诊断。Matlab仿真结果表明，此方法具有良好的抗干扰性，能够较好地诊断出变压器绕组状态。最后再结合某变压器具体实例进行相应验证。
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| 关 键 词： | 变压器 绕组变形 信息融合 CS-SVM 故障诊断 |
| 收稿时间： | 2016/11/19 0:00:00 |
| 修稿时间： | 2017/1/19 0:00:00 |
Information fusion and CS-SVM based research on diagnosis method for transformer winding deformation fault |
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| GAN Xisong,LI Yun,FU Chenghu,GUO Hui and YANG Tingyu.Information fusion and CS-SVM based research on diagnosis method for transformer winding deformation fault[J].Power System Protection and Control,2018,46(1):156-161. |
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| Authors: | GAN Xisong LI Yun FU Chenghu GUO Hui and YANG Tingyu |
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| Affiliation: | School of Automation & Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China,Sichuan Technology & Business College, Chengdu 611830, China,School of Automation & Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China,School of Automation & Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China and School of Automation & Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China |
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| Abstract: | It is easy for transformer winding to generate deformation after short-circuit fault, and traditional frequency response analysis or short circuit impedance analysis is one-sided during the process of the winding deformation detection. This paper proposes a diagnosis method for transformer winding deformation fault based on fault information fusion and Cuckoo Search Optimization based Support Vector Machine (CS-SVM). It integrates the inspection data related to winding deformation into SVM input sample and puts it in the CS-SVM trained according to artificial experience for diagnosis. According to Matlab simulation result, this method has preferable anti-interference property, which can diagnose the state of the transformer winding better. Finally, corresponding verification is conducted by combining specific example of certain transformer. |
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| Keywords: | transformer winding deformation information fusion CS-SVM fault diagnosis |
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