Discrete-Wavelet-Transform and Stockwell-Transform-Based Statistical Parameters Estimation for Fault Analysis in Grid-Connected Wind Power System

Abstract

Detection and assessment of unbalanced conditions in an early stages are of utmost importance for reliable and smooth operation of a grid-connected wind system. This article presents fault assessment in the grid-connected wind system. For this purpose, the grid-connected wind system has been simulated in MATLAB. All symmetrical and unsymmetrical faults have been considered for three different wind systems. The system current signal has been taken and normalized, then using discrete wavelet transform (DWT)-based statistical parameter analysis, unbalanced conditions have been detected. Total harmonic distortion (THD), interharmonics groups, and Stockwell transform (S-transform) based statistical parameter analysis has also been used for total assessment of unbalanced conditions, like presence of harmonics, classification of faults, etc. This article emphasizes fast detection and classification of all unbalanced conditions of the grid-connected wind system. Then, severity of different unbalanced conditions has been assessed by investigating the presence of harmonics using advanced signal-processing-based approach.