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作 者:陆启宇[1] 李腾飞[1] 王备[2] 王丰华[3]
机构地区:[1]国网上海市电力公司电力科学科学院,上海200437 [2]国网上海市电力公司市北供电公司,上海200072 [3]上海交通大学,上海200240
出 处:《计算机仿真》2017年第1期453-457,共5页Computer Simulation
摘 要:对变压器的绕组状态进行检测,可以有效预防变压器遭受突发短路冲击后绕组松动、形变等机械故障。当前检测方法不能有效的检测出微小非平稳信号,检测精度较低。本文从振动信号的非稳定性和强时变性出发,提出基于自适应EEMD算法的变压器绕组状态检测方法。首先利用白噪声频谱均匀分布的特点来均衡信号中的间歇区域,使用三次样条插值法对原信号的时间长度进行插值处理,得到端点效应后的上下包络线,以此求取上下包络函数以及信号序列局部极值点,通过IMF提取过程,将信号按照频率从高到低把每个特征信号分解出来,根据自适应波形匹配法实现延拓波形与原信号的光滑过渡,利用自适应波形延拓法解决EEMD固有的端点效应问题,从而实现对变压器绕组状态的检测。实验仿真证明,所提检测方法可以有效检测变压器绕组的状态变化,检测精度高,且具有很强的适用性。The burst short circuit shocking may result in mechanical faults of transformer such as winding looseness and deformation. It can prevent the mechanical faults to detect the winding state. Current method cannot detect the tiny non-stationary signal effectively. The detection precision is poor. This paper proposes a state detection meth- od of the transformer wingding based on the self-adaption EEMD algorithm from the point of non-stationary and strong time-varying characteristics of vibration signal. Firstly, the method uses equally distribution feature of white noise spectrum to balance the interval area in the signals and makes the interpolation process to the time duration of initial signal using the cubic spline interpolation. Then, it obtains the top and bottom envelope line after end effect, and gets the top and bottom envelope function and local extremum point of signal sequence. Moreover, it decomposes each feature signal according to frequency from high to low through the IMF extraction process and achieves the smooth connection of prolongation waveform and initial signal according to the self-adaption waveform method. Finally, it uses the self-adaption waveform prolongation to solve the intrinsic EEMD end effect and achieves the state detection of transformer winding. The simulation results show that the proposed method can detect the state change of transformer winding effectively and has high detection precision and strong applicability.
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