提升小波理论在铁路MOA在线监测系统中的应用

Application of Lifting Wavelet Theory on MOA Online Monitoring System in Power Supply of Railway

在铁路MOA在线监测系统中,铁道电网的高次谐波含量大、频率不稳定、电压波动大,使在线监测系统存在大量的干扰、噪声等问题。引入了提升小波理论,这是一种新的理论,与经典小波变换相比,具有小波构造简单、运算速度快和节省存储空间等优点。提升小波理论继承了第一代小波多分辨率的特性,不依赖傅立叶变换,很容易从正变换得到反变换。把提升小波理论应用到铁路MOA在线监测系统中,可以解决数字滤波、去除干扰和噪声等问题。运用MATLAB7.0对其进行仿真,得出提升小波是一种更为有效的分析方法。

In MOA online monitoring system in power supply of railway, since there are much of high harmonic wave in power supply of railway, frequency instability, voltage fluctuation, which make a lot of interference, white noise and harmonic in online monitoring system, it is difficult to resolve these problems using traditional method, so we introduce lifting wavelet theory to this system, which is a new theory comparing with traditional wavelet one and easy to construct, with quickly computational speed and lower memory space requirement, not depending on FFT, it inherit multi-resolution analysis of sutra wavelet and easy to get reverse transformation from positive one, this paper makes lifting wavelet applied in MOA online monitoring system in power supply of railway, which can accomplish digital filtering, get rid of interference and white noise etc, then using MATLAB7.0 to simulate , we can get the result that the lifting wavelet is an effect analysis way.