| 引用本文: | 黄南天,李富青,王文婷,等.输电线路故障层次化变步长Tsallis小波奇异熵诊断方法[J].电力系统保护与控制,2017,45(18):38-44. |
| HUANG Nantian,LI Fuqing,WANG Wenting,et al.A method of transmission line faults diagnosis based on Tsallis wavelet singular entropy with hierarchical variable step size[J].Power System Protection and Control,2017,45(18):38-44 |
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| 摘要: |
| 为提高熵方法输电线路故障信号时-频域的特征提取能力，提出层次化变步长Tsallis小波奇异熵(Tsallis Wavelet Singular Entropy, TWSE)方法用于电力系统故障诊断。首先，对采集到的电压信号进行小波分解与单支重构，构建时-频矩阵；之后，将奇异值分解与Tsallis熵理论相结合，对该时-频矩阵求滑动步长为1的Tsallis奇异熵，确定故障发生时刻；然后，对故障发生后1周期内的三相电压重构系数求滑动步长为1/4周期的TWSE，构建用于故障诊断的特征向量；最后，将TWSE特征向量输入到极限学习机(Extremly Learning Machine, ELM)分类器中，实现输电线路故障诊断。仿真结果表明，新方法具有更好的故障暂态信号特征表现能力，且分类结果不受故障时间、过渡电阻和故障位置等因素影响，相较基于小波奇异熵的线路故障诊断方法具有更好的诊断效果。 |
| 关键词: 故障诊断 小波变换 奇异值分解 Tsallis熵 极限学习机 |
| DOI：10.7667/PSPC160587 |
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| 基金项目:国家自然科学基金项目(51307020);2016年吉林省科技发展计划项目(20160411003XH，20160204004GX);吉林省教育厅“十三五”科技项目(吉教科合字[2016]第90号) |
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| A method of transmission line faults diagnosis based on Tsallis wavelet singular entropy with hierarchical variable step size |
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HUANG Nantian1,LI Fuqing2,WANG Wenting1,YU Zhiyong3,NIE Yonghui1
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(1. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;2. State Grid Zhejiang Ningbo Fenghua Electric Power Supply Company, Ningbo 315500, China;3. Economic and Technology Research Institute of State Grid Xinjiang Electric Power Company, Urumqi 830011, China)
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| Abstract: |
| In order to improve the capability of entropy method in time-frequency feature presentation of fault signals of transmission lines, a new method is proposed for power system fault diagnosis based on Tsallis wavelet singular entropy (TWSE) with hierarchical variable step size. Firstly, the collected voltage signals are transformed by wavelet decomposition and single branch reconstruction, which is used to construct time-frequency matrix. Secondly, the singular value decomposition theory combines with the Tsallis entropy theory, and the time-frequency matrix processed by TWSE with 1 sliding step size is used to determine the fault occurrence time. Then, the method calculates TWSE with 1/4 period sliding step size to obtain the feature vector for fault diagnosis from the one period after the fault happened of the three-phase voltage reconstruction coefficient. Finally, the TWSE feature vector is input to the classifier based on the extreme learning machine (ELM) to realize fault diagnosis of transmission line. Simulation results show that the new method has better feature representation ability for fault transient signal, and classification result is not affected by fault time, transition resistance and fault location. Compared with the SWSE fault diagnosis method based on wavelet singular entropy, the method of TWSE has better diagnosis effect. This work is supported by National Natural Science Foundation of China (No. 51307020). |
| Key words: fault diagnosis wavelet transform singular value decomposition Tsallis entropy extreme learning machine |
