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

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基于多維數(shù)據(jù)的線路電壓互感器在線監(jiān)測技術(shù)研究

來源:電工電氣發(fā)布時間:2022-07-18 13:18 瀏覽次數(shù):415

基于多維數(shù)據(jù)的線路電壓互感器在線監(jiān)測技術(shù)研究

徐衛(wèi)東,何文志,廖肇毅,劉勤鋒
(廣東電網(wǎng)有限責(zé)任公司東莞供電局,廣東 東莞 523000)
 
    摘 要:針對線路電壓互感器電壓波動受運行方式、負(fù)荷大小、線路長度等客觀因素的影響,從利用在線監(jiān)測技術(shù)替代線路電壓互感器傳統(tǒng)停電預(yù)試進(jìn)行研究。通過研究 110 kV 及以上輸電線路首末兩端電壓互感器實時采集數(shù)據(jù),以及電壓偏差范圍,采用神經(jīng)網(wǎng)絡(luò)算法對不同運行工況下的輸電線路電壓偏差進(jìn)行自適應(yīng)學(xué)習(xí)。結(jié)果表明,線路首末兩端電壓偏差符合正態(tài)分布曲線規(guī)律,神經(jīng)網(wǎng)絡(luò)方法制定輸電線路首末兩端電壓互感器的告警規(guī)則更加合理,經(jīng)故障案例驗證,證明線路首末兩端電壓互感器電壓閾值設(shè)定的準(zhǔn)確性,完善了電壓互感器在線監(jiān)測閾值設(shè)定的空缺。
    關(guān)鍵詞: 電壓互感器;神經(jīng)網(wǎng)絡(luò);誤差分析;在線監(jiān)測
    中圖分類號:TM451     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2022)07-0050-06
 
Research on Online Monitoring Technology of Line Voltage
Transformer Based on Multi-Dimensional Data
 
XU Wei-dong, HE Wen-zhi, LIAO Zhao-yi, LIU Qin-feng
(Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd, Dongguan 523000, China)
 
    Abstract: The voltage fluctuation of the line voltage transformer is affected by objective factors, such as operation mode, load size, line length, etc. This paper studied the perspective of using online monitoring technology to replace the traditional power failure pre-test technology of line voltage transformers. It collected real-time data and voltage deviation range by studying the initial and ending of the voltage transformer of 110 kV and above. Moreover, it used the neural network algorithm to self-adaptive learn the voltage deviation of the transmission line under different operating conditions.The results showed that the voltage deviation of the initial and ending of the circuit matched the normal distribution curve law, and the alarm rules formulated by the neural network method was more reasonable. The fault case verification proves the accuracy of the voltage threshold setting of the voltage transformers and completes the vacancy of the voltage transformer online monitoring threshold setting.
    Key words: voltage transformer; neural network; error analysis; online monitoring
 
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