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作 者:马文嘉 王丰华[1] 党晓婧 MA Wenjia;WANG Fenghua;DANG Xiaojing(Department of Electrical Engineering,Shanghai Jiao Tong University,Minhang District,Shanghai 200240,China;Electric Power Research Institute,Shenzhen Power Supply Co.,Ltd.,Shenzhen 518000,Guangdong Province,China)
机构地区:[1]上海交通大学电气工程系,上海市闵行区200240 [2]深圳供电局有限公司电力科学研究院,广东省深圳市518000
出 处:《电网技术》2021年第9期3755-3762,共8页Power System Technology
摘 要:针对变压器遭受短路冲击时的声信号,该文提出一种基于稀疏自适应S变换的变压器绕组状态检测方法。首先依据变压器短路冲击声信号的幅值谱确定了稀疏自适应S变换的窗函数,然后采用双向2维主成分分析对声信号时频特征矩阵进行了降维处理,据此定义了特征能量变化率来评判变压器的绕组状态。对某220kV变压器短路冲击声信号的计算结果表明:基于稀疏自适应S变换得到的声信号优化时频域特征具有分辨率高、能量聚集性强的优点,所定义的特征指标能有效反映变压器绕组状态变化。研究结果可为变压器短路冲击绕组状态检测提供重要依据。Based on the acoustic signals of a power transformer under the short-circuit impact, a detection method of the transformer winding condition based on the sparse adaptive S transform is proposed in the paper. First, the window function of the sparse adaptive S transform is determined according to the amplitude spectrum of the acoustic signals of the transformer under the short-circuit impact. Then the two-dimensional principle component analysis(2 DPCA) method is used to reduce the dimension of the time-frequency feature matrix. The energy change rate of the features is finally defined to detect the winding condition of the transformer. The calculation results of the acoustic signals of a 220 k V transformer under the short-circuit impulse test show that the optimized time-frequency features of the acoustic signals of the transformer under the short-circuit impulse obtained by the sparse adaptive S transform have the advantages of high resolution and strong energy concentration. The energy changing ratio of the time-frequency feature matrix can effectively reflect the variations of the transformer winding condition. The research results can provide an important basis for the detection of the transformer short-circuit impulse winding state.
关 键 词:变压器声信号 短路冲击 双向2DPCA 稀疏自适应S变换 能量变化率
分 类 号:TM721[电气工程—电力系统及自动化]
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