Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于深度学习与谐波谱相关分析的台区识别

来源:电工电气发布时间:2020-08-22 10:22 浏览次数:83
基于深度学习与谐波谱相关分析的台区识别
 
徐晓东1,吕干云1,鲁涛1,吴启宇2
(1 南京工程学院 电力工程学院,江苏 南京 211167;2 国网江苏省电力有限公司南京市溧水区供电分公司,江苏 南京 211200)
 
    摘 要:为了提高用户台区识别的效率和精度,提出了一种基于深度学习与谐波谱相关分析的台区识别方法。采集配变出口电压进行谐波频谱分析,并通过深度置信网络(DBN)的特征提取模型自适应提取配变电压特征谐波谱。提取用户端智能电表的电压特征谐波谱,利用谱相关分析法计算智能电表与配变间电压特征谐波谱的皮尔逊相关系数,进而通过谱相关程度对比判断用户所属台区和相别。选取南京市某低压配电网进行现场测试,实测结果表明,所提方法提高了用户台区和相别识别效率,为电网公司对台区精细化管理提供新技术。
    关键词:深度学习;特征谐波谱;谐波谱相关分析;台区识别
    中图分类号:TM715     文献标识码:A      文章编号:1007-3175(2020)08-0007-05
 
Transformer Area Recognition Based on Harmonic Spectrum Correlation Analysis and Deep Learning
 
XU Xiao-dong1, LYU Gan-yun1, LU Tao1, WU Qi-yu2
(1 School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167 , China;
2 Nanjing Lishui District Power Supply Branch of State Grid Jiangsu Electric Power Co., Ltd, Nanjing 211200, China)
 
    Abstract: In order to improve the efficiency and accuracy of user transformer area recognition, a transformer area recognition method based on deep learning and harmonic spectrum correlation analysis is proposed. Firstly, collected the output voltage of the distribution transformer to analyze the harmonic spectrum, and the characteristic harmonic spectrum of distribution transformer voltage is extracted adaptively by the feature extraction model of depth confidence network (DBN). Then, the voltage characteristic harmonic spectrum of the smart meter is extracted, and the Pearson correlation coefficient of the voltage characteristic harmonic spectrum between the smart meter and the distribution transformer is calculated by using the spectral correlation analysis method, and then the user's transformer area and phase are judged by comparing the spectral correlation degree. Finally, a low-voltage distribution network in Nanjing is selected for field test, and the actual test
results show that the proposed method can effectively complete the recognition of user transformer area and phase.
    Key words: deep learning; characteristic harmonic spectrum; harmonic spectrum correlation analysis; transformer area recognition
 
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