基于谐波滤波器组的电能质量扰动数据压缩方法

提出一种基于谐波滤波器组的电能质量扰动数据压缩方法,该方法采用谐波滤波器组实现原始信号中稳态分量与暂态分量的分离,并通过插值离散傅里叶变换(IpDFT)算法来高精度估计谐波分量参数,然后对确定的稳态分量和暂态分量分别采用参数量化和小波变换进行压缩。最后,利用实际的电能质量扰动数据测试了该方法,结果表明,该方法对于各种类型的电能质量扰动信号均有效,同时在压缩比相同时,与通用小波压缩方法相比具有更好的压缩性能。

Data Compression Method for Power Quality Disturbance Based on Harmonic Filter Bank

By using the harmonic filter bank(HFB)technique to separate the harmonic components from the transient ones in the power quality(PQ)disturbance signals, a data compression method for PQ disturbance based on HFB is proposed. The harmonic component parameters can be estimated with high precision by adopting the interpolated discrete Fourier transform(IpDFT)algorithm. The harmonic components are then compressed by parameter quantization, and the residue of disturbance is compressed by wavelet transform. The real PQ disturbance data are finally used to test the proposed method. The results show that the method is effective for various PQ disturbance data, and has even better performance than that of the standard wavelet compression method with the same compression ratio. This work is supported by National Natural Science Foundation of China(No. 51477124).