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

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基于深度學(xué)習(xí)與諧波譜相關(guān)分析的臺區(qū)識別

來源:電工電氣發(fā)布時(shí)間:2020-08-22 10:22 瀏覽次數(shù):711
基于深度學(xué)習(xí)與諧波譜相關(guān)分析的臺區(qū)識別
 
徐曉東1,呂干云1,魯濤1,吳啟宇2
(1 南京工程學(xué)院 電力工程學(xué)院,江蘇 南京 211167;2 國網(wǎng)江蘇省電力有限公司南京市溧水區(qū)供電分公司,江蘇 南京 211200)
 
    摘 要:為了提高用戶臺區(qū)識別的效率和精度,提出了一種基于深度學(xué)習(xí)與諧波譜相關(guān)分析的臺區(qū)識別方法。采集配變出口電壓進(jìn)行諧波頻譜分析,并通過深度置信網(wǎng)絡(luò)(DBN)的特征提取模型自適應(yīng)提取配變電壓特征諧波譜。提取用戶端智能電表的電壓特征諧波譜,利用譜相關(guān)分析法計(jì)算智能電表與配變間電壓特征諧波譜的皮爾遜相關(guān)系數(shù),進(jìn)而通過譜相關(guān)程度對比判斷用戶所屬臺區(qū)和相別。選取南京市某低壓配電網(wǎng)進(jìn)行現(xiàn)場測試,實(shí)測結(jié)果表明,所提方法提高了用戶臺區(qū)和相別識別效率,為電網(wǎng)公司對臺區(qū)精細(xì)化管理提供新技術(shù)。
    關(guān)鍵詞:深度學(xué)習(xí);特征諧波譜;諧波譜相關(guān)分析;臺區(qū)識別
    中圖分類號:TM715     文獻(xiàn)標(biāo)識碼:A      文章編號: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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