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作 者:郏琨琪 胡海敏 邱曙光 高梵清 蔡祯祺 周琪 JIA Kunqi;HU Haimin;QIU Shuguang;GAO Fanqing;CAI Zhenqi;ZHOU Qi(Urban Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 200080,China;Shanghai Fuze Energy Technology Co.,Ltd.,Shanghai 200001,China)
机构地区:[1]国网上海市电力公司市区供电公司,上海200080 [2]上海服泽能源科技有限公司,上海200001
出 处:《自动化应用》2025年第2期218-220,共3页Automation Application
摘 要:针对现有故障识别方法放电信息识别不清晰的问题,研究了基于多传感器信息融合的高压开关柜局部放电故障识别。通过数据预处理滤除干扰信号,以提取有用的信号信息。选取BP神经网络方法在局部放电识别中进行学习,使得网络的输出能更准确地逼近期望值,达到较好的融合处理。采用TDOA的方法来定位开关柜内部局部点故障的三维坐标,计算出局部放电源的具体位置。利用修正后的距离差,计算误差更小的局部放电源故障坐标,完成对高压开关柜局部放电的故障识别。结果表明,开关柜在特定检测点上的异常数据尤为显著,其最大相对幅值高达20dB,且伴随清晰可辨的放电高频信号,并能准确识别出高压开关柜内部存在的局部放电现象,达到了良好的应用效果。Aiming at the problem of unclear discharge information recognition of existing fault recognition methods,a partial discharge fault recognition method for high-voltage switchgear based on multi-sensor information fusion was studied.Filter out interference signals through data preprocessing to extract useful signal information.Selecting the BP neural network method for learning in partial discharge recognition enables the network's output to more accurately approximate the expected value and achieve better fusion processing.Using the TDOA method to locate the three-dimensional coordinates of local point faults inside the switchgear and calculate the specific location of the local discharge power source.Using the corrected distance difference,calculate the coordinates of local discharge source faults with smaller errors,and complete the fault identification of partial discharge in high-voltage switchgear.The results show that the abnormal data of the switchgear at specific detection points is particularly significant,with a maximum relative amplitude of up to 20 dB,accompanied by clear and distinguishable discharge high-frequency signals,and can accurately identify the partial discharge phenomenon inside the high-voltage switchgear,achieving good application results.
关 键 词:多传感器 信息融合 高压开关柜 局部放电 故障识别
分 类 号:TG115.28[金属学及工艺—物理冶金]
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