Power quality disturbance detection and classification using Chirplet transforms

In this paper, a new approach is presented for the detection and classification of PQ disturbance in power system by Chirplet transforms(CT), which is the generalized forms of Fourier transforin(FT), short-time Fourier transform(STFT) and wavelet transform(WT). WT and wavelet ridge are very useful tools to analyze PQ disturbance signals, but invalid for nonlinear timevarying harmonic signals. CT can detect and identify voltage quality and frequency quality visually, i.e., according to the contour of CT matrix of PQ harmonic signals, the harmonies can be detect and identify to fixed, linear timevarying and nonlinear time-varying visually. It is helpful to choose appropriate WT to analyze harmonics. Simulations show the contours of CT can effectively detect harmonic disturbance occurrence time and duration. Finally, it is validated that the harmonies of the stator current fault signal of the bar-broken electric machine is nonlinear time-varying, and tend to stable status in a short time.