Power quality disturbances classification with improved multiresolution fast s-transform

The noise is the most important factor to affect the recognition accuracy of power quality disturbances. The time-frequency modular matrix obtained from S-transform has the characteristics of gray image. Therefore, the classification accuracy of disturbances can be improved by two-dimensional mathematical morphology de-noising method. Firstly, an improved multi-resolution fast S-transform with different time-frequency resolutions was constructed according to the time-frequency distribution characteristics of modular matrix. It was used to reduce the computation complexity and improve the ability of time-frequency feature presentation. Secondly, morphological open operator with a line type, zero angle structure element was used in the high frequency area of the modular matrix to immune noise affection after threshold filtering. Finally, a decision tree classifier was designed based on five features which were extracted from the original signals, Fourier spectrums of original signals and time-frequency modular matrix of multi-resolution fast S-transform. The new method can recognize the noise signal without disturbances and 12 types of disturbances including 6 types of complex disturbances. The comparison of simulation experiments shows that the new method has better noise immunity and more suitable for disturbances recognition in the noise environments.