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作 者:王立辉[1] 罗拓 宋亮亮 任旭超 张文鹏 赵凯 WANG Lihui;LUO Tuo;SONG Liangliang;REN Xuchao;ZHANG Wenpeng;ZHAO Kai(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;State Grid Jiangsu Electric Power Co.,Ltd.Research Institute,Nanjing 211103,China;State Grid Jiangsu Electric Power Co.,Ltd.Extra High Voltage Branch,Nanjing 211102,China)
机构地区:[1]东南大学仪器科学与工程学院,江苏南京210096 [2]国网江苏省电力有限公司电力科学研究院,江苏南京211103 [3]国网江苏省电力有限公司超高压分公司,江苏南京211102
出 处:《电力工程技术》2022年第5期227-232,共6页Electric Power Engineering Technology
基 金:国家重点研发计划资助项目(2021YFB2501603)。
摘 要:通过时频变换方法分解光纤电流互感器(FOCT)输出信号,获取渐变故障信号特征,是故障分析的关键步骤。针对FOCT渐变性故障信号时域跨度大且劣化过程呈随机性的特点,对输出信号进行跨间隔采样,利用小波包分解算法,根据故障信号频段实现故障信号特征提取,利用相关评价指标对时域特征参数进行筛选,得到表征FOCT劣化趋势的最优特征参数。针对信号特征维度高的特点,提出主元分析法对高维特征降维处理,满足故障特征辨识快速性的需求。实验结果表明:使用6层小波包分解算法,得到64个包含不同频段信号的子序列,对比各个频带能量占比来确定互感器运行状态,能够实现有效辨识渐变性故障特征。Decomposing the output signal of fiber optical current transformer(FOCT)with time-frequency conversion is the key step to obtain the gradual fault characteristics.Aiming at the characteristics of FOCT gradual fault signal with large time domain span and random deterioration process,the output signal is sampled across intervals,and the wavelet packet decomposition algorithm is used to extract fault signal features according to the frequency band of the fault signal.The characteristic parameters are screened to obtain the optimal characteristic parameters that characterize the FOCT degradation trend.The principal component analysis method is proposed to reduce the dimensionality of high-dimensional features,the problem of high signal feature dimensionality is solved,and the need for fast fault feature identification is met.Experiment results show that the wavelet algorithm can decompose the signal into various frequency bands and obtain 64 sub-sequences containing signals of different frequency bands.The operating status of the transformer is determined by the energy ratio of each frequency band of the wavelet signal,and the gradual fault identification is realized.
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