Nonstationary signal pattern recognition using fast time-time filtering and decision tree

Abstract

This paper proposes a fast Time-Time (TT) filtering transform for analysis and pattern recognition of nonstationary signals. A fast TT-transform algorithm is developed with different types of frequency scaling, band pass filtering and interpolation techniques to reduce the computational cost. The new time-time transform uses dyadic and selective scaling that facilitates the extraction of relevant features from time-varying signals for recognizing their patterns. The extracted features are then passed through a decision tree based classifier for the identification of the signal patterns. Various real world simultaneous power signal disturbances have been simulated to prove the efficiency of the technique. The simulation results show superior performance of the new TT-Transform while classifying overlapping disturbance patterns. Because of the new fast TT -transform algorithm and a relatively simpler classifier methodology, this technique can be used for real time localization, detection, and classification of various power quality events including other nonstationary signal time series belonging to speech, biomedical signals, etc.

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

“…Despite having certain advantages, TT‐transform is having somewhat higher complexity impacting the speed of detection and classification process. Therefore, the complexity of the process is reduced by computing ST only at the chosen frequencies which are selected according to dyadic frequency scaling . According to dyadic frequency scaling, only few frequencies are chosen for accomplishing the fast computation of TT‐transform.…”