赵莉华,刘浩,罗小春,张振东,黄小龙.基于TVFEMD与中心频率算法的变压器绕组松动故障诊断方法[J].电测与仪表,2020,57(15):19-25. zhao li hua,liu hao,luo xiao chun,zhang zhen dong,huang xiao long.Diagnosis Method for Winding Looseness Fault of Transformer Based on TVFEMD and Center-frequency[J].Electrical Measurement & Instrumentation,2020,57(15):19-25.
基于TVFEMD与中心频率算法的变压器绕组松动故障诊断方法
Diagnosis Method for Winding Looseness Fault of Transformer Based on TVFEMD and Center-frequency
考虑到变压器振动信号为非平稳的周期信号，文中引入时变滤波经验模式分解（Time Varying Filter for EMD，TVFEMD）和中心频率算法处理。在一台10kV实验变压器连接额定阻性负载条件下，测试得到绕组正常和松动两种状态下的振动信号，利用TVFEMD对去噪后的振动信号进行模态分解，得到多个模态函数（IMF），然后通过中心频率算法筛选50~700Hz频段内的IMF，最后求取各阶IMF能量特征，将其分为低频能量和高频能量，二者比值作为特征量。研究结果表明利用本文特征量提取方法可以实现绕组松动状态的诊断，并且该特征量能够排除变压器常见运行条件变量，如负载率、功率因数、电流谐波的影响，降低了误判风险。
英文摘要:
Considering that the transformer vibration signal is non-stationary periodic signal, this paper introduces Time Varying Filter for EMD (TVFEMD) and Center-frequency algorithm processing. The 10kV experimental transformer with the condition that transformer is connected to the rated resistive load, vibration signal of transformer under winding normal and looseness are obtained. The denoised vibration signal is modally decomposed by TVFEMD to obtain multiple modal functions (IMF), and then, the Central-frequency algorithm is used to select IMF which the frequency between 50~700Hz. Finally, calculating the energy characteristics of IMF, and dividing it into low-frequency energy and high-frequency energy, and the ratio of them is used as the feature quantity. The research result shows that the feature quantity of the vibration signal under winding looseness is 2~3 times of the normal state, which has a certain degree of discrimination. At the same time, to a certain extent, the feature quantity can reduce the influence of operating conditions, such as loading factor, power factor and current harmonic, so the risk of misjudgment is reduced.
