| 引用本文: | 刘可真,束洪春,于继来,田鑫萃,骆 逍.±800 kV特高压直流输电线路故障定位小波能量谱神经网络识别法[J].电力自动化设备,2014,34(4): |
| LIU Kezhen,SHU Hongchun,YU Jilai,TIAN Xincui,LUO Xiao.Fault location based on wavelet energy spectrum and neural network for ± 800 kV UHVDC transmission line[J].Electric Power Automation Equipment,2014,34(4): |
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| 摘要: |
| 固有频率与故障距离之间存在数学关系，故障行波暂态能量在固有频率附近较集中，其暂态能量包含丰富的故障距离信息。利用人工神经网络（ANN）的非线性函数逼近拟合能力，建立直流输电线路故障定位的ANN模型。利用小波变换的等距特性提取单端线模电压7尺度的小波能量，并将其作为样本属性对神经网络进行训练、测试。所提方法将不易提取的固有频率点特征转化为容易提取的频带特征，提高了测距的可靠性。数字实验结果表明，所提方法在不同过渡电阻和不同故障距离下均能准确测距。 |
| 关键词: 特高压输电 直流输电 固有频率 物理边界 小波能量谱 人工神经网络 故障定位 |
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| 基金项目:国家自然科学基金资助项目（50977039，50847043，90610024，50467002，50347026，51267009，U1202233）； 云南省自然科学基金重点资助项目（2005F0005Z，2008ZC016M，2010Z20）； 云南省科技攻关项目（2003GG10） |
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| Fault location based on wavelet energy spectrum and neural network for ± 800 kV UHVDC transmission line |
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LIU Kezhen1,2, SHU Hongchun1,2, YU Jilai1, TIAN Xincui2, LUO Xiao2
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1.School of Electrical Engineering and Automation，Harbin Institute of Technology，Harbin 150001，China;2.Faculty of Electric Power Engineering，Kunming University of Science and Technology，Kunming 650051，China
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| Abstract: |
| The inherent frequency of fault traveling wave is mathematically associated with fault distance and its transient energy containing rich information about fault distance is concentrated around this frequency. Because of its fitting capability for non-linear function，an ANN （Artificial Neural Network） model of HVDC line is built to locate its faults. Based on the equidistant characteristic of wavelet transform，the transient energy spectrum of line voltage modulus at one end is extracted in seven scales，which are used as the samples to train and test the ANN model. The proposed method takes the inherent frequency band，instead of point，to extract fault information，which is easier and more reliable. Results of digital test show faults at any line position and with any transition resistance can be accurately located. |
| Key words: UHV power transmission DC power transmission inherent natural frequency physical boundary wavelet energy spectrum artificial neural network electric fault location |
