Power quality analysis adopting optimal multi-resolution fast S-transform

In order to meet the requirement of type recognition and parameter estimation in power quality disturbance signal analysis, this paper proposes an Optimal Multi-resolution Fast S-transform (OMFST) method. Firstly, the relationship between the disturbance parameter estimation errors and the kurtosis at the start and end locations of the disturbances in the time-amplitude curve and frequency-amplitude curve under different time-frequency resolutions is analyzed. The optimal window width adjustment factors in different frequency ranges are determined according to the deviation maximization method, and the cubic spline interpolation method is used for fitting; the optimal window width required for the type recognition and parameter estimation of different disturbance signals is adjusted automatically. Secondly, OMFST processing is conducted according to the fundamental frequency of the disturbance signal and the middle and high frequency ranges in which the disturbance signal locate. Finally, the disturbance classifier based on fuzzy decision tree is constructed based on the 5 features extracted from the original signal, the Fourier transform spectrum of the original signal and the time-frequency modular matrix of OMFST. The new method can recognize 13 types of power quality signals and estimate the disturbance parameters. Simulation and actual test data analysis show that the new method can meet the requirement of the parameter estimation of compound power quality disturbance signal. The parameter estimation error is lower than those of the generalized S-transform and etc. Meanwhile, the proposed new method remains good classification accuracy.