【作者】 赵来军；

【导师】 何俊佳；

【作者基本信息】 华中科技大学 ， 高电压与绝缘技术， 2005， 硕士

【摘要】 变压器局部放电在线监测对于及时、准确了解设备绝缘状况,防止事故的发生有着十分重要的意义。实现在线监测的关键是如何从强噪声中检测出微弱的随机局部放电信号,因此干扰的识别和抑制一直是实现变压器局部放电在线监测急需解决的难点。局部放电信号是一种非平稳的时变信号。传统的信号处理方法在处理这种信号时往往缺乏良好的时频局部化特性,难以有效地从噪声和干扰中提取出局部放电信号。小波分析方法在时域和频域同时实现了局部化特性,特别适合处理奇异信号。本文首先针对变压器在线监测中可能出现的局部放电信号的类型和干扰信号的种类作了一定的讨论,通过对其进行频谱图研究,归纳出了不同干扰信号和局部放电信号所特有的指纹特征,建立起了局部放电仿真模型和干扰模型。其次着重从软件滤波方面,阐述了小波消噪的基本原理和目前存在的问题,利用小波变换对变压器局部放电在线监测中出现的周期性干扰信号和白噪声加以抑制。由于窄带周期干扰的频率范围很宽,自适应滤波器的参数设置比较困难,有时甚至会导致算法不稳定。本文利用小波包良好的分频能力和可根据信号特点自动选择基的优点,首次提出了基于小波包自适应滤波算法,以用于抑制窄带周期干扰:利用小波包的分频特性先将信号分解到不同的频段上,然后对各频段的信号施以自适应滤波,由于信号被分解到不同的频段,各频段内的窄带干扰频率相差有限,所以可以根据各频段信号的特性采用最佳的滤波参数,以达到较好的滤波性能。论文深入研究了基于小波变换模极大值原理去除局放信号中白噪干扰的方法,根据去噪后信号小波系数的特点和模极大值的特性,采用分段三次样条插值算法,重构小波系数。在插值之前,先给出了一种对模极大值进行预处理的方法。然后结合Matllat重构算法,可以较好的恢复信号。与经典的交替投影方法相比,此方法可以得到更高的信噪比增益和更小的相对均方误差。研究表明,小波分析能有效、准确、稳定去除周期窄带干扰和白噪干扰,再结合其他信号处理技术,可很好地去除混杂在局放信号中的各类干扰。更多还原

【Abstract】 On-line Partial Discharge(PD) monitoring for transformer is meaningful in precise interpretation of insulating condition and accident prevention .One of the most important problems in on-line PD monitoring for transformer is how to detect the weak stochastic PD pulses from the strong background noises. PD signal is a kind of non-stationary time-variable signal. Frequency Spectrum analysis technology is a traditional method for signal detecting and analysising. This method is effective when signal is stationary and spectrum is different from noise. But in fact, what we face with usually is nonstationary signal .It is necessary that every frequency component in every moment need to be analyzed. So the traditional method have some disadvantages and it is difficult to detect the PD signal from the interference effectively. The wavelet analysis method has simultaneous time-domain and frequency-domain resolution capability and it can be used to process the singular signal especially. To study the characteristics of PD pulses and interferences that may appear in the on-line monitoring for transformer and explore their time-frequency distribution more deeply, the wavelet analysis is applied and lots of literatures are referenced. Based On the different pulses and interference’s finger-print map,the pulses and interferences simulation models are established. After analyzing materials and previous work,some disadvantages are found in processing the no calm wave signal of PD using the wavelet analysis method at the present time。A new adaptive algorithm based on wavelet analysis to suppress narrow bandwidth noise is proposed. The usual adaptive filter is one of the best algorithm in suppressing sinusoidal noises in PD signal processing. But it is difficult in setting the parameters of the adaptive filter in PD on-line monitoring due to wide frequency range of narrow bandwidth noise, and it may be unstable sometimes when the parameters set improperly. Study shows that the new algorithm has better performance and stability compared to usual adaptive filters. When a denoised process is performed on a signal with wavelet transform modulus maximum principle,how to reconstruct a satisfactory signal from the remained modulus maxima is an important subject. In this paper,an analysis is made on the relationship between the modulus maxima and wavelet coefficients. Then the fact that modulus maxima are actually discrete samples of wavelet cofficients in a specific sense is obtained. By preprocessing the modulus maxima we get a new set of pseudo modulus maximum sequence with which a new piecewise cubic spline interpolating algorithm to reconstruct the wavelet coefficients is presented.Compared with the alternate projection method,this algorithm is simple and easy to implement and can get higher reconstruction signalSNR gain and smaller RMSE than the altermate projection method,so it is a practical and efficient algorithm. The processing results of numerical simulation demonstrate that the proposed method has tremendous potential for extracting partial discharge signals from the noisy background, thus providing reliable signals, on the basis of which, further study can be explored.更多还原