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机构地区:[1]中南大学物理科学与技术学院,湖南长沙410083 [2]北京华电云通电力技术有限公司,北京100069
出 处:《电力自动化设备》2010年第3期81-85,共5页Electric Power Automation Equipment
基 金:国家自然科学基金资助项目(50277039)~~
摘 要:为及时发现电力变压器绕组变形等潜伏性故障,需要实时监测变压器绕组的状态。建立了变压器绕组的数学模型,利用变压器原、次边的电压、电流信号对变压器的短路阻抗进行在线辨识。其方法是:实时采集模型变压器原、次边的电压、电流信号后,针对电压、电流传感器采集信号的特点,应用小波变换除去噪声,再利用基于离散傅里叶变换的高精度相位识别法辨识各正弦量间的相位差,得到各负载情况下变压器绕组等效电路的短路阻抗。利用模型三相变压器搭建的变压器绕组状态监测平台进行实验,结果表明,变压器绕组未发生状态改变时,不同负载情况下短路阻抗的辨识差别不超过0.64%;若变压器绕组发生变形及匝间短路等故障,短路阻抗的变化量达到5.6%以上,证明所提出的监测算法是有效的。The status of transfomer windings should be monitored in real-time to detect their potential faults. The mathematical model of transformer windings is established and its short-circuit impedance is on-line computed with the voltage and current signals at both sides. The signals mentioned above are sampled in real-time and denoised by the wavelet transformation according to the characteristics of voltage and current sensors. By precisely scaling the phase differences based on discreet Fourier transform,the short-circuit impedances of the transformer windings are identified under various load conditions. Experiments are carried out on the windings monitoring platform built with model transformer and the results show that,the difference between short-circuit impedance measurements under various load conditions does not surpass 0.64 % when there is no winding status change,whereas if there is interturn short-circuit or winding deformation,change of short-circuit impedance reaches up to 5.6 %,proving the effectiveness of offered on-line monitoring algorithm.
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